Distributed Energy Resources in Local Integrated Energy Systems: Optimal Operation and Planning

Distributed Energy Resources in Local Integrated Energy Systems: Optimal Operation and Planning reviews research and policy developments surrounding the optimal operation and planning of DER in the context of local integrated energy systems in the presence of multiple energy carriers, vectors and multi-objective requirements. This assessment is carried out by analyzing impacts and benefits at local levels, and in distribution networks and larger systems. These frameworks represent valid tools to provide support in the decision-making process for DER operation and planning. Uncertainties of RES generation and loads in optimal DER scheduling are addressed, along with energy trading and blockchain technologies.

Interactions among various energy carriers in local energy systems are investigated in scalable and flexible optimization models for adaptation to a number of real contexts thanks to the wide variety of generation, conversion and storage technologies considered, the exploitation of demand side flexibility, emerging technologies, and through the general mathematical formulations established.

• Integrates multi-energy DER, including electrical and thermal distributed generation, demand response, electric vehicles, storage and RES in the context of local integrated energy systems
• Fosters the integration of DER in the electricity markets through the concepts of DER aggregation
• Addresses the challenges of emerging paradigms as energy communities and energy blockchain applications in the current and future energy landscape
• Proposes operation optimization models and methods through multi-objective approaches for fostering short- and long-run sustainability of local energy systems
• Assesses and models the uncertainties of renewable resources and intermittent loads in the short-term decision-making process for smart decentralized energy systems

• Language: English
• Publisher: Elsevier Science
• Release date: Feb 27, 2021
• ISBN: 9780128242148

Book preview

Distributed Energy Resources in Local Integrated Energy Systems

Optimal Operation and Planning

Edited by

Giorgio Graditi

Department of Energy Technologies and Renewable Sources of ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy

Marialaura Di Somma

Department of Energy Technologies and Renewable Sources of ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy

Table of Contents

Cover image

Title page

Copyright

List of contributors

Chapter 1. Overview of distributed energy resources in the context of local integrated energy systems

Abstract

Abbreviations

1.1 Introduction

1.2 Distributed energy resources

1.3 Grid side aspects

1.4 Emergent paradigms and solutions

References

Chapter 2. Architectures and concepts for smart decentralised energy systems

Abstract

Abbreviations

2.1 Introduction

2.2 Why decentralizing the energy system?

2.3 Development of the decentralized architecture

2.4 Grid-secure activations for ancillary services (real-time control)

2.5 ELECTRA Web-of-Cells control concept

2.6 Post-primary voltage control

2.7 Balance restoration control

2.8 Balance steering control

2.9 Adaptive frequency containment control

2.10 Inertia control

2.11 Decentralizing the DA/ID energy market clearing and grid prequalification of ancillary services

2.12 What is next: evolution of roles and responsibilities necessary for decentralization the European regulatory framework

2.13 Conclusions

References

Chapter 3. Modeling of multienergy carriers dependencies in smart local networks with distributed energy resources

Abstract

Abbreviations

Nomenclature

3.1 Introduction

3.2 Internal multicarrier dependency in a smart local system

3.3 External dependencies in a smart local system

3.4 Interdependency modeling

3.5 A case study on interdependent MES model

3.6 Conclusions

References

Chapter 4. Multiobjective operation optimization of DER for short- and long-run sustainability of local integrated energy systems

Abstract

Abbreviations

Nomenclature

4.1 Importance of multiobjective operation optimization for short- and long-run sustainability of local integrated energy systems

4.2 Multiobjective optimization for the operation of a local integrated energy system

4.3 Case study: eco-exergetic operation optimization of a local integrated energy system for a large hotel in Beijing

4.4 Operation optimization of multiple integrated energy systems in a local energy community

4.5 Conclusions and key findings

References

Chapter 5. Impact of neighborhood energy trading and renewable energy communities on the operation and planning of distribution networks

Abstract

Abbreviations

Nomenclature

5.1 Introduction

5.2 A distributed approach for the day-ahead scheduling of the LEC

5.3 Implementation and numerical tests

5.4 Distribution network planning model considering nonnetwork solutions and neighborhood energy trading

5.5 Application of the planning model to case studies and analysis of the results

5.6 Conclusions

Acknowledgment

References

Chapter 6. Fostering DER integration in the electricity markets

Abstract

Abbreviations

6.1 Distributed energy resources as providers of flexibility services

6.2 The regulatory framework for the participation of distributed energy resources in different electricity markets

6.3 Flexibility needs in power systems

6.4 The market value of flexibility in the distribution system

6.5 Local energy markets

6.6 Conclusions

References

Chapter 7. Challenges and directions for Blockchain technology applied to Demand Response and Vehicle-to-Grid scenarios

Abstract

Abbreviations

7.1 Introduction

7.2 The blockchain technology

7.3 The energy blockchain: current trends and possible evolutions

7.4 Laboratory setup for energy blockchain testing

7.5 Conclusions

Acknowledgment

References

Chapter 8. Optimal management of energy storage systems integrated in nanogrids for virtual nonsumer community

Abstract

Abbreviations

Nomenclature

8.1 Introduction

8.2 Energy storage systems as distributed flexibility

8.3 The energy storage system in a nanogrid: the configuration

8.4 Optimal energy management for virtual nonsumers nanogrid community

8.5 The energy storage systems for grid ancillary service

8.6 Case study

8.7 Conclusions

References

Chapter 9. Demand response role for enhancing the flexibility of local energy systems

Abstract

Abbreviations

Nomenclature

9.1 Introduction

9.2 Demand response programs for local energy systems

9.3 Flexibility assessment of local energy systems in the presence of energy storage systems and DR programs

9.4 Energy management framework for DER integrated distribution networks

9.5 Simulation results

9.6 Conclusion remarks

Acknowledgment

References

Chapter 10. The integration of electric vehicles in smart distribution grids with other distributed resources

Abstract

Abbreviations

Nomenclature

10.1 Introduction to electric vehicles and charging infrastructures

10.2 Integration of electric vehicles in smart distribution grids

10.3 Vehicle-to-Grid

10.4 Conclusions

References

Chapter 11. Assessing renewables uncertainties in the short-term (day-ahead) scheduling of DER

Abstract

Abbreviations

Nomenclature

11.1 Introduction

11.2 RES uncertainties description and assessment

11.3 Uncertainties affecting system resilience

11.4 Assessing renewables uncertainties in the short-term (day-ahead) scheduling of DER

11.5 Discussion and conclusions

References

Chapter 12. Load forecasting in the short-term scheduling of DERs

Abstract

Abbreviations

Nomenclature

12.1 Introduction

12.2 New trends in load forecasting

12.3 Trans-active energy systems with DERs

12.4 Short-term scheduling of DERs in demand side

12.5 Conclusions and future thoughts

References

Chapter 13. Conclusions and key findings of optimal operation and planning of distributed energy resources in the context of local integrated energy systems

Abstract

Index

Copyright

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List of contributors

Amir Ahmarinejad,     Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Ali Arefi,     College of Science, Health, Engineering and Education, Murdoch University, Perth, WA, Australia

Angel A. Bayod-Rújula,     Department of Electrical Engineering, University of Zaragoza, Zaragoza, Spain

Alberto Borghetti,     Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy

Morris Brenna,     Department of Energy, Politecnico Di Milano, Milan, Italy

Chris Caerts,     VITO/EnergyVille, Genk, Belgium

M. Caruso,     Exalto Energy & Innovation Srl, Palermo (PA), Italy

João P.S. Catalão

Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal

Faculty of Engineering of the University of Porto, Porto, Portugal

Gianfranco Chicco,     Politecnico di Torino, Torino, Italy

Andrés Cortés

TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

Department of Electrical Engineering, University of the Basque Country, Bilbao, Spain

Javid Maleki Delarestaghi,     College of Science, Health, Engineering and Education, Murdoch University, Perth, WA, Australia

Marialaura Di Somma,     Department of Energy Technologies and Renewable Sources of ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy

Zhao Yang Dong,     School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia

V. Efthymiou,     FOSS Research Centre for Sustainable Energy of University of Cyprus, Nicosia, Cyprus

Federica Foiadelli,     Department of Energy, Politecnico Di Milano, Milan, Italy

Jesús Fraile-Ardanuy,     Information Processing and Telecommunication Center (IPTC-SISDAC), Universidad Politécnica de Madrid, Madrid, Spain

P. Gallo,     Department of Engineering, University of Palermo, Palermo (PA), Italy

Inés Gómez,     TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

Giorgio Graditi,     Department of Energy Technologies and Renewable Sources of ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy

M.G. Ippolito,     Department of Engineering, University of Palermo, Palermo (PA), Italy

Mohammad Sadegh Javadi,     Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal

Joseba Jimeno,     TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

Weicong Kong

School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia

ALDI Stores, Sydney, NSW, Australia

Fengji Luo,     School of Civil Engineering, University of Sydney, Sydney, NSW, Australia

Carlos Madina,     TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

Seyed Amir Mansouri,     Department of Electrical Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran

Daniele Menniti,     Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, Italy

Julia Merino

TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

Department of Electrical Engineering, University of the Basque Country, Bilbao, Spain

Andrei Z. Morch,     SINTEF Energy Research, Trondheim, Norway

Anna Mutule,     Institute of Physical Energetics, Riga, Latvia

S. Nassuato,     Regalgrid Europe S.r.l., Treviso (TV), Italy

Nilufar Neyestani,     Centre for Power and Energy Systems, INESC TEC, Porto, Portugal

Ali Esmaeel Nezhad,     Department of Electrical, Electronic, and Information Engineering, University of Bologna, Bologna, Italy

Carlo Alberto Nucci,     Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy

Camilo Orozco Corredor,     Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy

C.N. Papadimitriou,     FOSS Research Centre for Sustainable Energy of University of Cyprus, Nicosia, Cyprus

M. Patsalides,     FOSS Research Centre for Sustainable Energy of University of Cyprus, Nicosia, Cyprus

Anna Pinnarelli,     Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, Italy

E.R. Sanseverino,     Department of Engineering, University of Palermo, Palermo (PA), Italy

Maider Santos,     TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

G. Sciumè,     Department of Engineering, University of Palermo, Palermo (PA), Italy

Miadreza Shafie-Khah,     School of Technology and Innovations, University of Vaasa, Vaasa, Finland

Nicola Sorrentino,     Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, Italy

P. Therapontos,     Electricity Authority of Cyprus (EAC), Nicosia, Cyprus

N. Tomasone,     Regalgrid Europe S.r.l., Treviso (TV), Italy

V. Venizelos,     FOSS Research Centre for Sustainable Energy of University of Cyprus, Nicosia, Cyprus

Bing Yan,     Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY, United States

Jiajia Yang,     School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia

Dario Zaninelli,     Department of Energy, Politecnico Di Milano, Milan, Italy

G. Zizzo,     Department of Engineering, University of Palermo, Palermo (PA), Italy

Chapter 1

Overview of distributed energy resources in the context of local integrated energy systems

Gianfranco Chicco¹, Marialaura Di Somma² and Giorgio Graditi²,    ¹Politecnico di Torino, Torino, Italy ,    ²Department of Energy Technologies and Renewable Sources of ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy

Abstract

The present evolution of the energy systems is characterized by an increase of small-size solutions, with which new aggregations of local integrated energy systems are emerging. The current policies on cleaner and efficient energy systems are providing opportunities to the development of systems supplied by renewable energy sources, and to the combined production from multiple energy carriers. Regulatory evolutions are enabling the existence and operation of local energy markets and energy communities.

This chapter provides an overview of the relevant literature concerning the use of distributed energy resources in the context of local energy systems. The chapter is organized in three parts. In the first part, the aspects addressed include the deployment of local solutions to generate, store, and manage energy at small scale and micro-scale. The second part deals with grid-related aspects and solutions for optimal grid operation. The third part discusses various emergent aspects referring to local energy systems, markets and energy communities, which are gaining relevance in the present and future context of energy transition.

Keywords

Distributed generation; distribution systems; demand response; energy communities; energy transition; local energy markets; microgrid; multi-energy systems; renewable energy; storage

Abbreviations

DER Distributed Energy Resources

DG Distributed Generation

DMES Distributed Multi-Energy Systems

DR Demand Response

DS Distributed Storage

DSM Demand Side Management

DSO Distribution System Operator

EH Energy Hubs

ENTSO-E European Network of Transmission System Operators for Electricity

ESP Electricity Shifting Potential

EV Electric Vehicle

ICT Information and Communication Technology

IES Integrated Energy Systems

MES Multi-Energy Systems

MPER Maximum Profit Electricity Reduction

PV Photovoltaic

RES Renewable Energy Sources

RTP Real-Time Pricing

SNET Smart Networks for Energy Transition

TES Thermal Energy Storage

TOU Time-of-Use

V2G Vehicle-to-Grid

VPP Virtual Power Plants

1.1 Introduction

Distributed Energy Resources (DER) are technologies and means that can be deployed at the supply side or demand side of a Low Voltage or Medium Voltage electric distribution system to meet the energy and reliability needs of the user(s) served by that system. The DER components are partitioned into Distributed Generation (DG), Demand Response (DR), and Distributed Storage (DS). A particular type of DG is the one composed of Renewable Energy Sources (RES). From another point of view, DER contain both local generation resources connected to the distribution system or at the customer side of the meter, as well as demand-side resources such as load management systems or local energy storage. The latter are aimed at changing the shape of the electrical demand curve and reducing the internal demand of the consumer.

The development of local energy systems for DG and storage is not new. The conceptualisation of most of the relevant aspects was already conducted in the early Eighties [1,2]. Likewise, the principles of Demand Side Management (DSM) were defined in the Eighties as an attempt to build structured solutions for promoting the consumer participation to making the load patterns variable in time [3]. However, the development of DG, DS, and DSM solutions has been initially limited by the lack of suitable technologies available at accessible costs to the generality of the users. With respect to the situation that was in place in the Eighties in many industrialized countries, in the following years there have been significant changes that have introduced new perspectives and drivers for the successive evolution. The most significant drivers have been technical, economic, and social.

On the technical side, relevant advances have been conducted under the smart grid framework, in which Information and Communication Technologies (ICT) and distribution automation have been exploited for the modernization of the electrical infrastructure. In the smart grid framework, ICT plays a primary role, introducing increasingly higher capabilities for system monitoring and control, as well as data management (communication, storage, security, and analysis tools). In addition, the technologies for energy generation and storage have enhanced their performance and extended the range of the available sizes, up to making available small-scale and micro-power sources. The transition towards a new energy system characterized by a multitude of players has requested the creation of regulatory authorities to upgrade and extend the set of rules, to solve new issues concerning grid connection and more detailed protection system settings.

On the economic side, the advent and evolution of the competitive electricity markets, and the growing refinement of the regulation of the quality of supply, have created the unbundling of the electricity business with the separation of generation, transmission, distribution and retail, and have established new rules to incentivise or penalize the operators of the electrical system.

On the social side, the increasingly high attention towards environmental aspects has pushed the strong development of RES, also with the consequent improvement in the technical solutions, while the growing interest to promote consumer awareness and engagement has led to the introduction of new regulatory provisions concerning the demand side.

Most of the changes mentioned above have occurred during the restructuring of the electrical system. However, during the years there has been a progressive integration of the electrical system with other (nonelectrical) energy systems. This has led to develop and apply multi-energy solutions, in which there is a stronger integration among the energy network infrastructures and energy carriers, enabling the definition of proactive solutions to provide energy services. These solutions also include further interaction with the mobility infrastructure to encompass the diffusion of electric vehicles (EVs).

Recent developments towards enabling the formation of energy communities, also supported by appropriate policies, are driving the energy systems towards further integration. These developments are shaping the contours of an energy transition with a stronger cross-sector integration strengthening the link among climate, energy and mobility, in which the role of electricity is expected to increase in the final energy uses.

1.2 Distributed energy resources

1.2.1 Distributed generation based on different energy sources

In general, the choice of the DG technology to be installed is largely conditioned by the availability of the primary energy source. For a local energy system, this is even more a limiting factor. Another key aspect is the modularity of the DG plant, which has a double impact, (1) making it possible to construct the plant at different steps, while the portion of the plant already installed can be already profitable, and (2) enabling the operation of the plant as a cluster of modules, in which each module can be activated or not depending on the needs.

On the input side, the DG solutions can be partitioned by considering their type of supply, as DG supplied with:

a. System-based energy, such as network-based energy (e.g., electricity and fuels), or stored energy. The fuel supply is considered available in the limits of the continuity of supply of the networks, and the power that can be supplied is limited by the capacity of the network connection. Availability of a storage system allows time shifting of the supplied energy provision; however the power to be supplied has to be scheduled by taking into account the time-dependent constraints on the storage system.

b. Environment-based energy: in this case, the energy comes from a primary source such as solar irradiance or wind speed and direction, which are by nature uncertain and fluctuating in time.

A positive aspect of DER is the possibility of exploiting a mix of local energy sources, to benefit from the complementarities among these sources. This is particularly true for RES-based generation, for which the coupling with a storage system enables smoothing the fluctuations of the power output. The environmental impact of DER is assessed by considering the effects concerning emissions, noise, and visual impact. Technical and environmental aspects are then merged to establish the cost effectiveness of the DER solutions, in which the costs involved include installation, operation, and maintenance, with possible incentives and penalties established on the basis of the regulation in place.

On the modeling side, some details appearing in local energy systems cannot be approximated easily. For example, unbalanced loads are very likely to be found in local energy systems, making three-phase power flow analysis necessary in several cases. In addition, specific modeling is needed for many local units (generators and loads) controlled by power electronics, which cannot be simply modeled by using standard models (e.g., constant impedance, constant current, or constant power). In particular, aggregate models of loads such as the ones controlled by thermostats, electric vehicles, or storage units such as water heaters, have to be formulated in an effective way to represent the physical behavior of these units, also in dynamic conditions. For this purpose, the power node model [4] has been introduced as a modeling framework in which different components, including generic energy storage units, are represented in such a way to be used for both power system analysis and dynamic simulations.

1.2.2 Combined production of different energy carriers

DG applications such as cogeneration (typically with electricity and heat) and multigeneration (e.g., with electricity, heat, and cooling) are based on the combined generation of different energy carriers. The relevant paradigms referring to combined generation include:

• Integrated Energy Systems (IES), which focuses on the integration of DG equipment with thermally activated technologies. The IES program was launched in 2001 by the US Department of Energy, and includes laboratory-based applications, e.g., for composed systems including microturbine with heat recovery, air conditioning and ventilation, desiccant, and absorption chiller units [5].

• Energy Hubs (EH), which refers to the integrated delivery of different energy carriers in multi-carrier energy systems [6]. The EH framework was developed within the project Vision of Future Energy Networks, focusing on the long-term evolution of the energy systems.

• Multi-Energy Systems (MES), which address the efficient exploitation of the combined production of different energy carriers connected to energy networks [7–9]. The EH framework has been adopted and extended for MES and distributed multi-energy systems (DMES) to deal with environmental impact and flexibility aspects [10]. A detailed discussion on MES is provided in Chapter 3 of this book.

Common aspects of the IES, EH, and MES paradigms are the formulation of mathematical models of the individual components and of the whole system, and the use of these models for energy efficiency maximization. The matrix form of the EH model has become a very successful structured framework for the mathematical representation of the interactions among the components of the energy system, useful for defining an input–output model that takes into account the efficiencies of the components and the topological connections. Storage has been included in the EH model in such a way that its contribution is beneficial with respect to the basic EH model [11]. Another additional contribution has been included in the EH model to represent the load shifted for DSM purposes [12]. The EH model has been further extended to account for the dependencies among different energy carriers, enabled from availability of alternative solutions, based on different energy carriers, to provide the same final service [13]. In this case, the consumers may choose the energy carrier, with choices variable in time.

The combined production of multi-energy outputs, integrated into local energy systems, is an essential component for maximizing energy efficiency. Furthermore, it may allow improving the environmental impact by reducing the emissions of local and global pollutants. Suitable indicators have been formulated to calculate the relative variation of the equivalent fuel input (for energy efficiency purposes) or the relative variation of the mass of pollutant (for environmental impact calculations) with respect to the separate production of the same useful energy outputs [14]. Energy efficiency and environmental impact indicators can be used as objective functions to determine the energy inputs that maximize the energy and environmental performance of the MES.

The interactions between the different energy carriers may enable better reliability of the energy system. Reliability has to be evaluated by considering specific approaches, which can be partitioned into model-driven modeling and data-driven modeling [15]. In a MES, the interactions among the energy carriers have been studied by determining the multienergy feasible operating regions [16] and by defining the multi-energy node model as an extension of the power node model [10].

1.2.3 Demand response

DR is the evolution of a number of early approaches referring to the demand side. Among them, DSM was based on a set of principles, including peak shaving, valley filling, load shifting, flexible load shape, and strategic load reduction or growth [17]. The studies on DSM evolved in parallel with the analysis of possible time-dependent tariff rates, aimed at using the tariff lever to induce the consumers to shifting their electricity consumption to off-peak hours. Among these rates, Time-Of-Use (TOU) rates are predefined for different periods (e.g., peak hours, intermediate hours, off-peak hours) are kept constant for a relatively long period (e.g., one year). An evolution of the TOU concept is Real-Time Pricing (RTP), with variation of the electricity rates during time (e.g., at each hour) defined in a closer advance (e.g., daily). The further evolution is Spot Pricing, where the instant at which the rate is conceptually determined is immediately before the instant of consumption [18]. In all these early approaches, the role of the consumer is to take decisions considering the rate or price information provided by an external entity.

Since the beginning of the third millennium, the restructuring of the electricity business has started some actions intended to involve the consumers to a larger extent. DR has been introduced as the voluntary reduction of the electricity use in response to price signals or incentives, aimed at improving the effectiveness of the electricity supply in response to specific needs (e.g., reducing the demand peaks in critical periods, with respect to a suitably defined baseline). Indeed, the introduction of social and psychological components for representing the behavior of the user makes the DR models more accurate [19] and enables better assessment of incentive-based DR [20]. The variety of the users that may be more or less interested in DR programmes [21], as well as the possible missing information due to communication inaccuracies, requires the adoption of tools able to handle incomplete information [22].

A detailed view on the DR programmes for DER integration in local energy systems is presented in Chapter 9 of this book.

1.2.4 Distributed storage

The classical view of the electrical service considers a just-in-time commodity, for which the generation follows the load (including the system losses). In this view, storage is generally not included, or has a minor role. For large electrical systems, this view is still valid, as the size of the storage systems available today is rather limited. However, for small local energy systems the situation is different. The size of the locally available storage in some cases is sufficient to change the paradigm from just-in-time to time-adjustable commodity, conceptually reaching the condition in which load follows generation. Ideally, with a sufficient amount of storage there would be no net power exchanged with the grid. However, in practice the capacity, energy, and ramp-rate constraints of the storage units impose limits to the storage system operation. Furthermore, the time-adjustable paradigm has to cope with the uncertainty and fluctuation of the generation from RES, which needs higher demand-side flexibility to exploit more generated power from RES.

The main distinction among the storage technologies is based on their power-based or energy-based usage [23]. The power-based usage implies fast provision of the service, with short times and relatively high power needed from the infrastructure. As such, it is more suitable for ensuring continuity of supply (in local energy systems through electric batteries, supercapacitors or flywheels), or for automotive applications. The energy-based usage implies slow provision of the service, with long times and relatively low power needed from the infrastructure. This usage is more suitable for applications that provide energy system services. In local energy systems, batteries, or hydrogen-based systems with fuel cells, can support frequency control. An interesting solution is the adoption of flow batteries, in which power and energy can be decoupled depending on the size of the stack to provide more power, and on the size of the electrolyte tanks to provide more energy [24]. Other promising solutions come from vehicle-to-grid (V2G) applications, with the deployment of the electricity stored in the batteries of the EVs, provided that appropriate infrastructure to manage the network connection with battery charging and discharging is in place. The integration of EVs as a DER component in the distribution grids is addressed in Chapter 10 of this book. For long-term energy storage, in local energy systems there are power-to-X solutions (e.g., power-to-liquid and power-to-gas), even though availability of these solutions for small-scale applications is still rather limited.

In addition to the storage of electricity, in local energy systems there are viable applications for thermal energy storage (TES) solutions, in which thermal energy is stored to create heat or cooling buffers [25,26]. TES systems have a slower response with respect to electrical storage systems, because their thermal capacity increases the thermal time constant. In addition, the stored heat or cooling cannot be used at high distances from the storage location, because of the increase in the temperature variations and of the corresponding thermal losses at higher distances, while a form of TES can be provided by the interaction with district heating and district cooling systems [27]. Furthermore, the efficiency of the TES system could be relatively low, and thermal standby losses between the storage medium and the environment are not negligible. However, in local applications, TES systems may have benefits from their relatively low cost of installation and maintenance, and low pollutant emissions. Moreover, TES systems can be used to enhance the operational flexibility of buildings [28] and distributed systems [29]. In addition to the many applications of MES-integrated heat storage, cold TES systems can be used for shifting the peak loads in buildings applications [30]. As such, TES systems are an important asset for local energy systems, also enabling distributed energy storage at specific locations within energy communities.

The management of energy storage systems in an energy community is discussed in Chapter 8 of this book.

1.3 Grid side aspects

1.3.1 Evolution of the grid connection issues and standards

Since the beginning of the diffusion of DER, grid connection has been the major issue for enabling the development and deployment of DER solutions, in particular for DG. Starting from a situation in which the electrical distribution networks were designed and operated in a centralized way, the connection of new DG plants introduced power injections not controllable by the distribution system operator (DSO). Because of this, new technical rules had to be issued by the regulatory authorities to establish viable conditions for authorizing the grid connections.

In the early period of DER integration (before the end of the Nineties), the power injected in the grid was relatively low, and did not cause particular technical problems to the voltage profiles, nor led to excessive growth in the short circuit currents. Hence, the perturbations to the distribution network were low, and there were even benefits given by the reduction of the net power load (i.e., demand minus generation), which reduced the currents flowing in the lines and the corresponding losses and voltage deviations from the rated voltage. In the further decade, there was a growth of the DER installations, in particular DG, with growing impact on the grid, even though critical cases with high node voltages or high short circuit currents were found only occasionally. In these periods, the regulatory requirements imposed to switch off the DG immediately in case of fault in the distribution network. The Standard IEEE 1547 [31] first introduced the technical specifications criteria and requirements applicable to all DER technologies with aggregate capacity of 10 MVA or less at the point of common coupling, interconnected to distribution systems. The Standard IEEE 1547 set up the basis for further regulatory developments at the national and International level.

In the following years, the DER diffusion had an advanced growth, also because of significant incentives to promote RES generation for DG, and the number of cases reaching technical limits for the DER diffusion increased considerably. In particular, the overall effect of the disconnection of the DG after a fault was seen at the transmission system level as the sudden steep increase of the net load, which could lead to critical situations for the security of the national electrical systems, impairing the stability of the transmission system. On 18 July 2011, a letter from the President of ENTSO-E launched the alert on the security issues caused by the automatic frequency disconnection settings of PV systems, encouraging the national authorities to implement remedial actions. This letter started several actions that led to modify the national standards, passing, in case of fault in the distribution network, from the situation of must switch off DG to the opposite need for keeping DG connected in parallel with the grid also in emergency and grid restoration conditions, with the introduction of the so-called fault ride-through capability limits. In practice, the protection systems at the grid interface and the inverters had to become less sensible to frequency variations, to keep the local systems connected to the grid also for frequency variations between 47.5 and 51.5 Hz.

After this radical change in the way to consider DER connection to the networks, most national standards were upgraded, and included the explicit definition of the user (e.g., the subject that uses the network to inject and/or take electricity), with its classification into active user (for production) when the user exploits rotating or static equipment to convert any form of useful energy into electricity and is connected to the network, and passive user otherwise (the specific standards contain more refined definitions, also indicating possible exceptions). The national standards also indicated the details for the settings of the protection devices, while actions for global harmonization of the standards at the international level are in progress, and the Standard IEEE 1547 series was enriched with more detailed documents during the years. In particular, the standards define the feasibility of islanding operation of a portion of the grid, in which the islanded part of the grid is only supported by DER. Islanding may be nonintentional (i.e., with the formation of an island after an outage), or intentional (allowing independent operation of the island during an interruption of the external supply). The main issue is that, in general, the DER could not ensure adequate frequency and voltage support, stability and quality of supply. In particular, the possible voltage support from DG units is limited by the reactive power capability of the local generation system. For this reason, nonintentional islanding is typically prohibited, and the nonintentional islands have to be detected and eliminated as fast as possible, also to avoid the presence of energized portions of the network during the maintenance carried out in the service restoration process. In case of intentional island, the DER connected to the island have to satisfy the loads and the load variations without experiencing dynamic problems in their voltage and frequency control systems.

1.3.2 Microgrids and local energy networks

The development of local energy systems is challenging the paradigm of providing the main supply from large distribution networks managed by the DSO. Further paradigms currently in use are:

• Virtual Power Plants (VPP): coordinated control and possible optimization of the operation of the resources in a local system, based on a framework conceptually established in 1997 [32].

• Microgrids: possibility of constructing and operating small distribution systems in a way independent of the grid [33,34].

• Nanogrids, as smaller microgrids supplied in direct current [35], which serve a local system, even a single building [36]. The nanogrids may also be interconnected among them or with larger systems [37]. More details are provided in Chapter 8 of this book.

• Web of cells: a decentralized control scheme is applied to portions of the network that can operate in grid-connected or islanded modes [38]. More details are provided in Chapter 2 of this book.

In the approaches mentioned above, the connection with the main grid is not necessarily absent, and may be considered especially in emergency conditions. However, the design of the network has to be carried out in such a way that the operation and survival of the local energy system does not depend on the connection to the distribution system. In this respect, the availability of local energy systems is significant for reducing the vulnerability of the electricity system in case of major events or deliberated malicious attacks.

The trend to developing micro-energy systems could raise the concern about the progressive reduction of the users that remain connected to the distribution grid, leading to grid defection issues [39]. This aspect is crucial and depends on the value the users give to the service provided by the grid, mainly linked to reliability aspects, for both electricity and gas networks [40].

The main objectives of VPPs are to enhance the visibility of the DER, provide suitable interfaces among the local components, activate distributed control strategies, promote the adoption of ICT solutions, address the optimal use of the available capacity, and study the interactions with the energy markets. Specific aspects of the VPP include the definition of characteristics similar as a plant connected to the transmission system (with possible meshed structures used during operation and the design of appropriate control and protection schemes), the management of a portfolio of DER, the establishment of generation schedules, the definition of the internal operating cost structure (which is a private information of the owner), and the possible provision of system services. The VPP concept is typically dedicated to control aspects, and does not necessarily imply the presence of a local energy system network independent of the distribution network.

Conversely, microgrids are small distribution systems containing generation, load, and storage, which can operate totally separated from the main distribution system (autonomous mode) or connected to it (nonautonomous mode). A microgrid is conceptually different with respect to intentional islands. In fact, intentional islands are normally connected to the network and may operate separately only when required (e.g., to assist the system restoration after a fault). On the other hand, microgrids operate normally as independent systems and may be connected to the network (in nonautonomous mode) in emergency conditions, to improve the continuity of supply after a fault in the network or microgrid, or according to economically convenient electricity prices for energy or reserves [41]. The operation of autonomous micro-grids is critical, because of possible voltage, frequency control, or stability issues. The demand for and supply of energy within the microgrid is monitored from a control center to optimize the use of DER. Appropriate redundancy and diversification in the energy supply mix is beneficial to reduce the critical cases as much as possible. The main advantages of the microgrids are high availability, possible combined management of the energy mix (electricity, heat, cooling, gas, water, hot water, etc.), modular operation planning, possible resource optimization inside the microgrid, synergies for personnel resources, primary energy purchase, maintenance, billing, and supply services, possible increase in economic efficiency, possible integrated energy bill with fixed cost reductions for the consumers and higher social acceptability, as well as reduced vulnerability of the local energy system due to the presence of many local resources. For these reasons, the microgrids are well suitable to be used in local energy systems, such as rural communities far from the existing power grids, industrial users with many local and scattered sites, industrial parks, island communities, power providers in developing countries lacking of infrastructure, and energy communities.

One of the crucial points to ensure viable operation of the local energy systems is to guarantee appropriate coordination among the control systems of the active users. The main operational issues occur when the local generation units are owned and managed independently of each other. This is likely to happen, because the objectives of managing the local units (maximizing the efficiency and profitability of the local system) are generally conflicting with those of a central coordination (e.g., network losses minimization, or voltage support optimization). As a consequence, there could be little or no coordination among the controls, and the local unit protections could not interact within an overall protection scheme, thus creating possible conflicting situations inside the microgrid. This noncoordinated situation could cause substantial worsening of the microgrid operation, e.g., for the voltage profiles. As such, a centralized control strategy would be advisable. An alternative solution is to establish a decentralized control strategy, in which each user is represented as an individual entity (e.g., an agent) that follows its own objectives. In this case, the interactions among the agents have to be structured very carefully, to keep the system operation on the correct line. A further issue is the low inertia that could exist in microgrids due to the many inverter-based microgrid interfaces.

Microgrid control strategies can be classified into three levels [42]: (1) the primary control, based on local output variables (voltages, currents, and frequency), which provides power sharing among the generation units through droop control; (2) the secondary control, based on the DER operating status and forecasts, which sends commands to the loads and dispatchable units; and (3) the tertiary control, based on RES and price forecasts, which sends long-term set points to coordinate the operation of the microgrid and of the distribution system to which the microgrids are connected.

For nanogrids and web of cells, the reduction of the scale of the system and the presence of a small number of DER connected makes it crucial to solve the issues referring to the controllability of such small systems by using refined power electronics and appropriate control systems.

1.3.3 Integration among energy networks

In general, the energy networks of different energy carriers do not share all the nodes, and the paths to reach the nodes are different [43]. This happens mainly because of the independent planning of the energy networks. However, even in the prospect of planning new multi-energy networks, maintaining different paths is a positive aspect to reduce the energy system vulnerability and improve resilience in case of extreme events, by guaranteeing different paths to reach the users.

Interactions between the electrical network and other networks are enabled by the presence of nodes with multi-energy systems, e.g., with MES with or without storage, district heating networks, district cooling networks, or power-to-X solutions. Multi-period multi-energy scheduling of coupled electrical, heating, and natural gas energy networks can be formulated [44]. In particular, smart gas networks have two main features: (1) the presence of smart metering and control systems, and (2) the possibility to allow the injection of nonconventional gases in the pipelines (without exceeding the related limits).

1.3.4 Analysis and optimization of the grid operation with local energy systems

Local energy systems are generally characterized by smaller networks with respect to large distribution systems. This simplifies some issues referring to the network analysis. In particular, for relatively small networks with meshed structure and radial operation, it is possible to determine all the radial network structures by using suitable computational techniques [45]. In this way, the optimization problems based on network reconfiguration can be solved by finding out the global optimum, without the need of executing dedicated algorithms of numerical computation or meta-heuristics. This possibility is viable for numbers of radial configurations of the order of millions, however there could be local energy networks with more complex structures in which the exhaustive search approach is not effective because of excessively long computation times. In these cases, the dedicated algorithms mentioned above are needed.

More importantly, in the presence of RES it becomes essential to model the effects of uncertainty. These effects can be addressed by:

1. executing Monte Carlo analyses, in which the statistical characteristics of the uncertain variables are represented together with the availability of the components, solution methods such as the probabilistic power flow based on a deterministic algorithm are run for a high number of times, and the statistics of the results provide the final outputs;

2. adopting stochastic programming models in the solver [46]; or

3. resorting to robust optimization methods, when the parameters of the problem are known only within given bounds [47].

When the uncertainty on the input parameters becomes too high, the use of probabilistic methods is not justified, because the standard deviation of the results would be so high to make the whole analysis substantially meaningless. The large-scale uncertainty that occurs in this case [48] is tackled by using scenario analyses. In practice, to represent different but foreseeable alternatives for the uncertain input variables, the analyst constructs a number of scenarios. Each scenario is then assigned a weight that represents its relative importance. Detailed aspects of RES uncertainty are discussed in Chapter 11.

The optimization methods are defined by their objective function(s) and constraints. In the presence of many objectives, it is possible to:

1. Formulate an overall single objective function, in which all objectives are represented with comparable quantities (e.g., an economic variable, by assigning a unity cost and an appropriate weight to each objective), and these quantities are summed up together.

2. Consider the objective functions separately, without merging them into a unique objective function. The objective functions are then handled through Pareto analysis, by identifying the nondominated solutions that can be seen as compromise solutions. Multiobjective operational optimization with DER for integrated energy systems is discussed in Chapter 4 of this book.

The typical problem for microgrid operation is microgrid scheduling. The objective (to be minimized) is the operation cost of local DERs, including the power exchange with the grid, to supply the microgrid loads in a certain period of time (typically one day). Multiobjective formulations are possible (e.g., considering cost and greenhouse gas emissions as two objectives). The microgrid loads and RES-based local generations are determined from forecasts. The major challenges refer to (1) the management of uncertainty, as microgrids are relatively small, and having a good prediction of RES what happens in a small area is not simple; (2) the coupling in time introduced by unit commitment (e.g., ramping constraints) or storage management; and (3) the use of possible new structures such as loop-based or meshed microgrids, which needs redesigning the protection and control systems. In case of connection to the distribution system, the possibility of reaching the condition in which the local system injects power into the distribution system, causing a reverse power flow in case of excess of local generation, has to be considered. Details of load forecasting for short-term DER scheduling are presented in Chapter 12 of this book.

Where possible, optimization has been formulated as a linear problem. In this case, the existing solvers allow obtaining the true optimal solution, typically without scalability limits for relatively small local systems. In more general problems, lack of convexity of the domain, the presence of integer variables, and nonlinear formulations are more challenging, and require the adoption of suitable solvers.

A relevant entity for the management of a local energy system is the aggregator. The task of the aggregator is to build a portfolio of clients (consumers or prosumers) that are globally able to provide a power profile with tradable bands of consumption (or local production), convenient to access profitable market options [49]. In general, a multienergy player may serve as a DER aggregator that manages the interactions between the wholesale energy market and