This paper proposes a distributed coordinated voltage control scheme for distribution networks with distributed generation (DG) and on-load tap changer (OLTC). In this scheme, static synchronous compensators (STATCOMs), DG units and OLTC are coordinated to regulate voltages of all buses to be close to the nominal value in the distribution network, 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 (GP) method. The local controller optimizes the reactive power outputs of DGs and STATCOMs according to local voltage and reactive power measurements, and still achieves the optimal coordination of DG units and STATCOMS in a decentralized manner without a central controller or communication between local controllers. The OLTC control scheme is designed to correct the long-term voltage deviations based on model predictive control (MPC) while minimizing the number of operations. The local controllers send the calculated reactive power references of DG and STATCOMs to the OLTC controller, which achieves distributed coordinated voltage control and mitigates the computation burden. A distribution network with two 20 kV feeders and 8 DG units was used to validate the control performance of the proposed coordinated voltage control scheme.

This paper presents a multiobjective optimization method to jointly optimize the planning and operation of a grid-tied microgrid (MG) with various distributed generation sources such as wind turbine and photovoltaic arrays with the assistance of demand side management. In order to explore the economy and demand variety, the problem is formulated as a double-objective optimization to minimize the total annual cost and to maximize the customer satisfaction. To solve the multiobjective optimization problem, a fuzzy satisfaction-maximizing method is adopted to convert the original problem into a single objective optimization problem and a mixed integer linear programming algorithm is then used to solve the problem. To verify the proposed solution, various case studies have been carried out and compared. The results show that the proposed method is effective in minimizing the cost of an MG without sacrificing the satisfaction of customers.

A novel two-stage sequential disaster recovery strategy for resilient cyber-physical distribution power systems (CPDPSs) is proposed with consideration of cyber-physical collaborative optimization. In the first stage, a mixed-integer linear programming (MILP) model is formulated based on the integration of a transportation network (TN), a cyber network (CN) and a physical network (PN) to achieve efficient load recovery. Additionally, a ‘repair-event-triggered’ mechanism is proposed to facilitate effective and collaborative optimization among physical maintenance crews (PMCs), cyber maintenance crews (CMCs), and a distribution network recovery model (DNRM). In the second stage, PMCs and CMCs are rescheduled to repair the remaining damaged components with the lowest time cost after all the power supplies are restored. The effectiveness and applicability of this proposed model are validated using the IEEE 33 bus system and the PG&E 69 bus system.

Power generated by the natural gas (NG) is a promising option for solving the restrictions on the development of the power industry. Consequently, the high interdependence between NG network and electricity network should be considered in this integration. In this paper, a day-ahead scheduling framework of integrated electricity and NG system (IENG) is proposed at a distribution level based on the fast alternating direction multiplier method with restart algorithm considering demand side response and uncertainties. Within the proposed framework, the detailed model of the IENG system at a distribution level is established, where the NG flow equation is processed by incremental linearization method to improve the computational efficiency. The objective is to minimize the operation costs of the entire system. With consideration of the uncertainties of distributed generation and electricity load as well as the uncertainties from the NG load, a two-stage robust optimization model is introduced to obtain the worst case within the uncertainty set, which is solved by column and constraints generation algorithm. In addition, the demand-side response (DSR) model including the decentralized air conditioning (AC) load model and the centralized ice-storage AC load model is integrated into the scheduling framework. Finally, the proposed day-ahead scheduling framework is verified by numerical studies where the optimal scheduling schemes are obtained in different cases, both the effects of the uncertainties and the performance with introducing DSR to the system operation are analyzed.

An optimal control problem for the power management system is proposed for the more electric aircraft with consideration of the dynamics in the generators and the energy storage systems. Electrical power components included in the electrical power system of MEA such as the generator, the bus, the rectifier, and the battery system are modeled in this optimal control problem. Based on this formulation, a three-stage optimization architecture is introduced to obtain the optimal power references and the optimal control policies. In the first stage, an optimization problem for the power scheduling and allocation is solved as a mixed integer quadratic programming problem and the optimal power allocation schemes (power references) are obtained. In the second stage, an optimal control problem to minimize the fluctuations in power generation systems is proposed and solved via an off-policy integral reinforcement learning approach. In the third stage, the voltage restoration is applied to maintain the main-bus voltage in the on-board High Voltage DC system. Extensive case studies are carried out to illustrate the effectiveness of our proposed methodology. Simulations show that the optimal power references are obtained based on various requirements, and the optimal power control policy is achieved based on off-policy iterations.