A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

Abstract

Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems

“…By determining the proposed switching sequences at each hour, the optimal topology of the distribution system is resulted. As can be seen in all the final topologies in Table 5, the network radiality without any islanded buses is preserved, which meet constraints (20)- (23). Moreover, the maximum allowable number of switching actions constraint in the day, stated in (24), is met.…”

“…In case that bus j is the parent of bus i (β ij = 1) or bus i is the parent of bus j (β ji = 1), according to (21) the respective branch is in the spanning tree (X br = 1). In order to avoid islanded buses in the final topology, (22)- (23) state that except the substation bus, all the buses should have one parent.…”

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

“…The faulty region is isolated in case of a fault, and electrical loads with the highest priority are restored according to their levels of importance with regard to certain switching operations. Moreover, network reconfiguration (NR) can be performed for a variety of goals such as increasing DG penetration and thus fueling consumption reduction [7], meeting the highest possible energy demand [8], minimizing active power losses [9,10], reducing cost of energy and switching operations [11,12], improving power quality and reliability indices (e.g., mitigating voltage sag) [13], and system restoration with minimum loss in case of failure [14].…”

Optimal Strategy of Active Distribution Network Considering Source–Network–Load

“…Deng et al [25] present a mixed‐integer dynamic optimisation model for the optimal scheduling of ADNs with the objective of minimising the daily costs of electricity purchased from distribution substations. Esmaeili et al [26] develop a framework for operational model of distribution systems with dynamic reconfiguration considering coordinated integration of energy storage systems and demand response programmes to minimise the total costs including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads (RLs), and cost of exchanging power with the wholesale market. Xu et al [27] introduce the multi‐agent system and multi‐layer electricity price response mechanism to construct an optimisation model of the distribution network layer, direct coordination source–load layer, and indirect coordination micro‐grid layer.…”