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.

Chapter 13 proposes a hierarchical service restoration scheme to obtain service restoration plans based on the alternating direction method of multipliers (ADMMs). As a critical part of self-healing, service restoration aims to restore outage areas with minimal unsupplied demands. With the increasing complexity and size of active distribution networks, distribution system operators face a more complicated service restoration problem. Thus, it is important to obtain optimal service restoration plans and reduce computational complexity. To achieve this goal, a hierarchical service restoration scheme is proposed to obtain service restoration plans based on the ADMMs. Firstly, the optimal service restoration problem is formulated as an mixed-integer nonlinear programming (MINLP) model considering the switching sequence, distributed generation units, and controllable loads. Then, the MINLP model is transformed into an mixed-integer linear programming model by using linearization techniques, which is decomposed and solved using the proposed ADMM-based algorithm in a hierarchical manner. In the proposed scheme, each zone of the ADN has a local service restoration controller solving its subproblem with information from a central service restoration controller. The central controller solves a global coordination problem with information from all the zones. The proposed hierarchical service restoration strategy can obtain optimal service restoration plans and reduce computational complexity. By using the proposed hierarchical scheme, the computation time can be reduced substantially for large-scale active distribution networks.

Chapter 2 proposes a distribution locational marginal pricing (DLMP) method based on quadratic programming (QP) to alleviate day-ahead congestion in distribution networks with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs (DTs) and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. First, a QP-based DSO optimization model considering network constraints is formulated to calculate DTs. Then, based on DTs, the aggregator optimization model is formulated to determine aggregator energy plans. It is proven that, through the link of DTs, the DSO energy plan and the aggregator energy plan are equivalent. Thus, the aggregator energy plans respect network constraints, denoting that congestion is effectively resolved. Case studies on a distribution network with high penetration of electric vehicles and heat pumps validate the equivalence of solutions obtained with the DSO and aggregator optimization models and the effectiveness of the proposed QP-based DLMP for congestion management.

Chapter 15 proposes a hierarchical distributed voltage control (HDVC) scheme for active distribution networks (ADNs) with high penetration of distributed generation (DG) based on gradient projection (GP) method and alternating direction method of multipliers (ADMM). The reactive power outputs of several distributed generation clusters (DGCs) and DG units within each DGC are optimally coordinated to keep DG terminal voltages and the voltages of all critical buses of ADNs within the feasible range and mitigate voltage fluctuations. In the ADN layer, a decentralized reactive power control scheme based on GP is designed for the DGC, which regulates the voltages of all critical buses to be close to the rated value and mitigates the reactive power variations. In the DGC layer, the reactive power outputs of DG units are further optimized based on ADMM to minimize the voltage deviation of each DG terminal and track the reactive power reference from the DGC control. The proposed HDVC scheme regulates the voltages in a decentralized manner without communication between DGC controllers, while each DG controller only communicates with the corresponding DGC controller. This regulates the voltages in a completely decentralized manner and effectively reduces the computation burden of the DGC and DG controllers. A modified Finnish distribution network with eight DGCs was used to validate the control performance of the proposed HDVC scheme.

This chapter proposes a two-tier demand response scheme with flexible demand swap (FDS) and transactive control for real-time congestion management in distribution networks. First, in order to resolve congestion, a FDS model is formulated, considering customers’ requirements on energy rebound, that is, periods and amounts of energy rebound energy. Then, in order to implement the FDS, a transactive control-based interactive mechanism between aggregators and customers is developed, which considers customers’ willingness to provide flexibilities. The proposed two-tier demand response scheme can resolve congestion efficiently while considering customers’ requirements on energy rebound and willingness to provide flexibilities for congestion management.

Chapter 6 proposes an alternating direction method of multiplier (ADMM)-based market clearing strategy for the distribution-level flexibility market and an optimal flexibility bidding strategy for aggregators. First, an optimal flexibility bidding model for aggregators is formulated, which carefully models the energy payback condition and enables the aggregator to receive the maximum revenue with flexibility costs considered. Then, the market clearing model is formulated and solved by the ADMM algorithm, which allows the market operator to communicate with the distribution system operator to clear the market such that the market clearing solution respects network operation constraints without revealing network parameters to the market operator. Case studies validated that the proposed distribution-level flexibility market framework is effective to perform day-ahead congestion management of distribution networks and is profitable to aggregators and end users.

Chapter 8 proposes a distributed coordinated voltage control scheme for distribution networks with DG and on-load tap changer (OLTC). In this chapter, static synchronous compensators (STATCOMs), DG units, and OLTC are coordinated to mitigate voltage fluctuations and minimize the number of operations of OLTC while considering different temporal characteristics of voltage regulation devices. The optimization problem of coordinating DG units and STATCOMs is decomposed by the gradient projection method. The OLTC control scheme is designed to correct long-term voltage deviations based on model predictive control while minimizing the number of operations. Moreover, a unidirectional communication-based coordination mechanism between fast var devices and OLTC is designed. A distribution network with two 20kV feeders and 10 DG units was used to validate the control performance of the proposed coordinated voltage control scheme.

Chapter 14 proposes a distributed voltage control scheme for unbalanced distribution networks (DNs) with single-/three-phase distributed generations (SDGs/TDGs) based on distributed model predictive control (DMPC). The active and reactive power outputs of each phase of DG units are optimally coordinated to regulate the three-phase voltages of all buses to be close to the nominal value and mitigate voltage fluctuations. According to the operation conditions of three-phase DNs, two control modes are designed: preventive and corrective modes. In the preventive mode, the measured voltages of each phase are within the predefined limits, and only the reactive power outputs of DG units are optimally adjusted to minimize the voltage deviations of each phase of all critical buses from the nominal value and mitigate the reactive power variations, while in the corrective mode, both the active and reactive power outputs of DG units are optimized to minimize the voltage deviations and the curtailed active power of DGs. The relationship between voltage variation of each phase and power injection is determined using an efficient analytical sensitivity calculation method. The DG controller only exchanges information with adjacent controllers and solves the local DMPC problem. A modified Finnish distribution network with five SDGs and three TDGs was used to verify the control performance of the proposed distributed voltage control scheme.

This chapter presents a robust dynamic tariff (DT) method to alleviate day-ahead congestion in active distribution networks (ADNs). The DT method as a decentralized day-ahead congestion management method has been widely studied. In the DT method, it is assumed that the distribution system operator (DSO) and aggregators use the same energy requirement parameters, which might not be the case in practice due to the DSO’s forecast error. The discrepancy between the DSO’s forecast parameters and the aggregator’s accurate parameters leads to a certain level of uncertainty that needs to be handled when employing the DT method. Therefore, a robust DT method is proposed for day-ahead congestion management while dealing with the uncertainty in the DT framework. First, a three-level robust DT model is formulated to obtain a robust DT solution, based on which the network constraints are respected even in the worst-case scenario. Moreover, due to the nonconvexity of the three-level robust DT model, the robust DT model is reformulated as a two-level optimization model, and a heuristic solution method is developed to obtain the robust DT solution with an iterative procedure. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed robust DT method for day-ahead congestion management in distribution networks. The case study results demonstrated that the deterministic DT method may be ineffective due to the DSO’s forecast errors, whereas the proposed robust DT method can resolve congestion efficiently under uncertain conditions.

With the increasing penetration of distributed energy resources and more advanced control methods at the distribution level, active distribution networks are believed to play important roles in the future power system operations. The optimal coordination of the transmission and distribution system operations will benefit both parts of the grids. In this chapter, the coordinated operation of the transmission system and distribution systems will be thoroughly reviewed. An integrated operation framework will be presented for the transmission and distribution networks. The objective is to minimize the overall operation cost of the entire system, which includes reserve activation at both the transmission system and distribution systems. An alternating direction method of multiplier-based method will be adopted to provide a distributed solution approach to the proposed coordinated operation problem to support practical operations.