Optimal Operation of Active Distribution Networks: Congestion Management, Voltage Control and Service Restoration

By Feifan Shen, Zhaoxi Liu, Wenshu Jiao and 2 more

Optimal Operation of Active Distribution Networks: Congestion Management, Voltage Control and Service Restoration provides researchers and practitioners with a clear and modern understanding of how to optimize the economic, secure and resilient operation of active distribution networks. The book provides case studies, modern implementations and supporting flowcharts and code, along with current research in congestion management, service restoration and voltage control of active distribution networks. Chapter provide an overview of the active distribution network concept, present key approaches for the congestion management of active distribution networks, and cover approaches in uncertainty, coordination of DLMP, scheduled re-profiling, and more.

Other sections cover real-time congestion management and service restoration of active distribution networks.

• Reviews how to optimally operate active distribution networks under normal conditions and demonstrates worked solutions and contingency planning to mitigate unforeseen challenges
• Provides clear guidance on optimizing congestion management, voltage control and service restoration in DER-heavy systems
• Demonstrates how to implement distributed voltage control in systems using active distribution networks
• Provides an extensive body of methods, associated case studies, worked solutions and implementation discussions on how to embed best practices in engineering and research workflows

• Language: English
• Publisher: Academic Press
• Release date: Aug 29, 2023
• ISBN: 9780443190162

Feifan Shen

Feifan Shen received the B.Eng. degree in Electrical Engineering and Automation from Hohai University, Nanjing, China, in 2015, and the M.Eng. degree from the Department of Electrical Engineering, Wuhan University, Wuhan, China, in 2017, and the joint Ph.D. degree from the Department of Electrical Engineering, Centre of Electric Power and Energy, Technical University of Denmark, Lyngby, Denmark, and the Interdisciplinary graduate school, Nanyang Technological University, Singapore, in 2021. He is currently an assistant professor in the school of electrical and information engineering, Hunan University. His research interests include congestion management and self-healing of distribution networks, wind farm operation and control, and the application of AI in smart grid operation.

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1

Introduction of active distribution networks

Abstract

In a move to reduce carbon footprint and alleviate fossil fuel shortage issue, the distributed energy resources (DERs) will be largely deployed in modern power systems. However, the proliferation of DERs in the electrical distribution network (DN) introduces new challenges in reliable power supply to customers, which accelerates the transformation of the passive DN into the active distribution network (ADN) to increase supply flexibility and reliability. The ADN can take advantage of the information and communication technologies to proactively coordinate the operation and control of DERs, enabling a secure and efficient solution to the management and control of the DN.

Keywords

Active distribution network; distributed energy resources; advanced distribution system infrastructure

1.1 Introduction

In the next 25 years, it is expected that the world’s total energy consumption will increase by one-third, and the pressure on energy shortage is increasing rapidly [1]. In this regard, many countries have set up ambitious energy strategies to realize independence from coal, oil, and gas [1]. The energy strategy presents a wide breadth of energy policy initiatives to reduce the use of fossil fuels and increase the share of renewable energy. As driven by a range of energy policies, the distributed energy resources (DERs), such as photovoltaics (PVs), small wind power systems (WPSs), electric vehicles (EVs), and heat pumps (HPs), will be massively deployed in power systems in the future [2,3].

The development of smart grid brings in an unprecedented opportunity to promote secure and sustainable energy development, creating a reliable and efficient energy industry. The electric power distribution network (DN) transferring power between the bulk power system and end users has been a fundamental and important part of smart grid and should be given priority in developing the future power grid.

In electrical DNs, the DERs provide benefits to the DN operation, such as flexible power supply, line loss reduction, and peak load reduction. However, most DERs’ generations, such as PVs and small WPSs, are uncertain in nature, which results in a mismatch between power production and power consumption. Thus, the proliferation of DERs introduces new challenges in management and control of the DNs and in ensuring a reliable and high-quality power supply to consumers.

In order to make the most efficient use of the existing DN infrastructure and to manage DERs for reliable and secure power supply, the paradigm transformation from passive DNs to active distribution networks (ADNs) is accelerated.

1.2 The concept of active distribution network

This section presents the definition and architecture of ADNs.

1.2.1 Definition of active distribution networks

After the activities of CIGRE Working Group C6.11 Development and operation of Active Distribution Networks, a consensus definition of ADNs is proposed [4]:

ADNs have systems in place to control a combination of DERs, defined as generators, loads and storage. It is possible for distribution system operators (DSOs) to manage the electricity flows in a flexible network topology. DERs take some degree of responsibility for system support according to a suitable regulatory environment and connection agreement.

1.2.2 Architecture of active distribution network

ADN is a power distribution system with many active units at the load side that are managed and controlled at the individual level and are geographically distributed in nature. The main components of an ADN include the following [5]: distribution substations, primary distribution feeders, distribution transformers, metering and control equipment and systems, distributed generation units, and consumer units.

The ADN should have a controllable and scalable network structure, which has some features, including uncertain and rapid change of power consumption and supply from distributed units, bidirectional power flow, information interaction, application of smart electrical equipment, etc. As shown in Fig. 1.1, the ADN consists of two interdependent subnetworks: power networks (PNs) and communication networks (CNs) [6]. The PN consists of electrical equipment, such as generation units, distribution substations, electrical feeders, loads, etc. The CN consists of metering, monitoring, and control equipment, such as sensors, controllers, communication lines, data storage central units, actuators, etc. The ADN operation involves the physical process and the communication process, and the two processes are deeply dependent. The physical process deals with the scheduling of power generation and consumption, control of network configuration, etc., according to commands sent from the CN. The CN collects real-time information of electrical nodes and power equipment operation in the PN, and the central control station obtains control commands sent to control the actions in the PN.

Figure 1.1 Simplified architecture of AND.

1.3 Key technologies of active distribution network

Compared with traditional DNs, DERs are increasingly integrated into the ADN. The noncoordinated operation of distributed generation units, distribution power grid, and flexible demand consumption introduces bidirectional power flow, causes voltage violations and line overloading, and makes the self-healing of DNs more complicated. To deal with these issues, some key technologies are required to optimize the control and management of active generation and load units and to conduct rapid grid recovery in case of contingency in order to supply reliable energy to the consumer.

1.3.1 Congestion management

The traditional power system is moving toward a smart grid with high penetration of DG units and the increasing deployment of flexible demands, such as EVs and HPs. The noncoordinated operation of these flexible and controllable loads is changing the daily power consumption profile, which results in operational challenges for ADNs, including the congestion issue when a large number of flexible loads have power consumption at the same time [7]. For example, the simultaneous EV charging could potentially cause overloading of the electricity lines, including the feeder overloading issue (loading exceeds the feeder physical limit) and the voltage deviation issue (bus voltage deviation exceeds the deviation limit, typically +/−0.1 p.u.).

A huge number of congestion management methods have been developed to alleviate the congestion of ADNs. They can be categorized into three types: (1) network reinforcement; (2) direct congestion management; and (3) indirect or market-based congestion management. Network reinforcement is to expand or harden the existing feeders to improve their current carrying capabilities [8]. It requires an additional capital investment in power components, such as lines and transformers. In direct congestion management methods, the DSO directly controls the existing power system components to alleviate congestion, for example, through network reconfiguration by operating sectionalizing and tie switches [9], and/or through direct voltage control by adjusting on-load tap changers, and/or through direct active/reactive power control [10]. The indirect methods, namely market-based methods, are based on the existing or newly designed markets, such as the day-head/real-time energy market and the flexibility market. They aim to use price or incentive signals to motivate customers to change their power consumption patterns in order to alleviate congestion.

For price-based methods, the distribution locational marginal price (DLMP) extended from the transmission-level locational marginal price (LMP) concept was developed in [11–15] to alleviate distribution-level congestion. The DLMP-based method could motivate DGs to produce more power locally to reduce energy delivery from remote areas during congestion hours, which could alleviate congestion and at the same time increase DGs’ revenues. The DLMP is calculated through the energy market clearing problem, which is similar to the LMP calculation. However, how distribution-level energy market clearing is coordinated with transmission-level energy market clearing has not been addressed.

The dynamic tariff (DT) method [16–20], dynamic power tariff (DPT) method [21], dynamic subsidy (DS) method [22], and line shadow price (LSP) method [23,24] have been developed to alleviate congestion of ADNs through rescheduling flexible demands such as EVs and HPs. Unlike the DLMP-based method, the DT, DPT, DS, and LSP methods are not based on the market clearing problem and can be seamlessly integrated into the existing day-ahead energy market, for example, the Nordic spot market. These methods have a common feature that the final electricity prices, that is, spot prices plus DTs or spot prices minus DSs, are higher at congestion hours than those at uncongestion hours. Consequently, customers will shift power consumption at congestion hours to uncongestion hours in order to minimize energy costs, thereby alleviating congestion.

For incentive-based methods, the aggregators or customers are rewarded for rescheduling power consumption. A monetary incentive-based method was proposed in [25] to coordinate flexible demands to alleviate day-ahead congestion. The power consumption rescheduling is based on spot prices and additional incentives. Another type of incentive-based method is to establish a flexibility market, such as the flexibility clearing house (FLECH) [26] and the local flexibility market (LFM) [27–33]. In these methods, the DSO buys flexibility service products provided by the aggregators or customers to alleviate congestion. The FLECH is a service-oriented platform aiming to trade flexibility service products, such as Flexibility Service of Overload Planned and Flexibility Service of Overload Reserve. In addition, three types of advance demand products are defined in [34], including Scheduled Re-Profiling product, Conditional Re-Profiling product, and Bi-Directional Conditional Re-Profiling product.

The LFM in [27] is based on bilateral flexibility contracts with multiple contracts between each pair of agents. The contract trading process is carried out with an iterative process between the DSO and aggregators and between the aggregator and customers. The LFMs in [28–33] are pool-based markets where flexibility sellers offer flexibility service products in the market, and the market operator clears the market to satisfy the flexibility buyers’ flexibility requirements based on different market clearing models. The LFMs in [28–30] are cleared before the day-ahead energy market clearing, so the LFM clearing solution is used to adjust original energy schedules to alleviate congestion. In contrast, the LFMs in [31–33] operate after the day-ahead energy market clearing if the accepted energy schedules in the energy market result in congestion.

Real-time congestion due to forecast errors and unexpected power component failures should be addressed as well. Real-time congestion management is usually implemented 5–60 minutes prior to the operation time [34]. The existing real-time market-based congestion management methods are limited, and most of them are incentive-based methods. In [34], a flexible demand swap-based method was developed to use flexible demand swap to alleviate real-time congestion. The flexible demand swap represents spatial and temporal power consumption/generation change, which can address both the rebound effect and the imbalance issue. An agent-based real-time congestion management scheme was introduced in [35], in which DERs are represented by agents to send flexibility service bids to the DSO, and then the DSO selects flexibility bids with the minimized procurement cost to alleviate congestion. In addition, direct load control is considered as a backup option for real-time congestion management.

1.3.2 Voltage regulation

The large integration of DERs has a significant impact on the voltage profile of ADNs, bringing in a number of voltage regulation issues [36] because the reverse power flow caused by DGs could result in a severe voltage rise [37]. Furthermore, the intermittent and stochastic nature of renewables could lead to voltage fluctuations. Maintaining the steady-state voltage within permissible limits and ensuring voltage quality in the system are essential for the operation of ADNs. The voltage regulation can be categorized as short-term and long-term voltage regulation [38]. The sustained overvoltage or undervoltage can be considered as a long-term voltage problem. The short-term voltage problem involves a voltage dip at a duration between one half-cycle and sixty seconds, which is usually caused by a sudden fault occurrence or power flow fluctuation.

Current ADN voltage regulation schemes may be categorized as local, decentralized control, and centralized control strategies. The voltage control strategies based on a centralized algorithm have been widely studied, which can achieve optimal control performance [39,40]. In the centralized scheme, a central controller is required to collect the operation information of the whole network, solve the centralized optimization problem, and send control commands to each device [41]. With the increased number of controllable agents, the centralized scheme is confronted with a heavy computational burden, a single-point failure risk, and difficulty protecting the information privacy of DGs. Distributed voltage control can overcome these challenges as it computes in parallel and only needs communication between adjacent regions, and it has become an important research direction of voltage regulation in ADNs.

1.3.3 Grid recovery

As the final link between customers and utilities, DNs must have a reliable electricity supply to customers. However, any fault in the network will cause supply interruptions to customers downstream to the faulty portion of the network [42]. Statistics show that DNs contribute most to the unavailability of electricity supply to customers [43]. Hence, it is critical to improve the resilience of ADNs [44].

The Advanced Metering Infrastructure (AMI) and Distribution Automation (DA) provide ADN self-healing capability. After a fault, self-healing represents the ability of the network to restore itself automatically and intelligently to the best possible status with minimum human intervention based on a set of equipment, algorithms, and communication technologies. With the installation of AMI and DA devices, such as remote terminal units and intelligence electronic devices, the network is capable of constantly detecting its operating status and taking corrective actions in case of emergency. In the presence of a permanent fault, the self-healing scheme is able to detect the fault location and isolate the faulty portions. Then, it starts to restore out-of-service demands by adjusting DA devices, for example, opening or closing remote controllable switches and dispatching distributed generation units.

As an essential aspect and complicated task of self-healing, service restoration has drawn considerable attention. The service restoration methods that require communication systems can be categorized into three types [45]: (1) centralized method: Each local agent communicates with a central controller that performs computations based on overall system information; (2) distributed method: Each agent communicates with its neighbors to perform computations, and there is no central controller; and (3) hierarchical method: Agents communicate in a hierarchical structure to perform computations, and local agents communicate with a central controller.

The techniques used in the existing service restoration methods to solve the service restoration problem and obtain service restoration plans include expert systems [46–50], heuristic algorithms [51–59], metaheuristic algorithms [60–64], graph theory [65–69], mathematical programming with a centralized algorithm [70–73], mathematical programming with a distributed algorithm [74–75], and multiagent systems [76–79].

Generally, service restoration has the following three objectives.

• Objective 1: Restore as many deenergized loads as possible.

• Objective 2: Minimize service restoration time.

• Objective 3: Minimize line losses of the new configuration.

The aforementioned objectives have different priorities. Maximizing deenergized loads restored is the main goal of service restoration and has the first priority, especially for critical customers, such as hospitals and government sectors. To avoid posing too much discomfort to customers, service restoration time should be minimized, for example, by minimizing the number of switching operations, and has the second priority. Minimizing line losses has the last priority. Since the newly configured network would not last for a long period, loss reduction would not provide a significant benefit and can be disregarded. In summary, service restoration is a complex problem since it is (1) combinatorial due to a large number of switching actions; (2) constrained; (3) nonlinear due to nonlinear constraints; and (4) multiobjective.

1.4 Conclusions

The ADN is a promising solution to increase the energy supply reliability and efficiency of future power systems by taking advantage of information and communication technologies and advanced automation infrastructure. This section presents the ADN concept and briefly describes the main components and architecture of the ADN. The increasing integration of DERs brings challenges to the control and management of ADNs, such as voltage deviation, transmission line overloading, etc. Relevant advanced technologies are required to deal with these challenges to achieve the optimal operation of power systems, including novel model formulations and advanced algorithms for congestion management, voltage regulation, and grid service restoration.

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