Prevention and Survivability for Power Distribution Resilience: A Multi-Criteria Renewables Expansion Model

Abstract

Distributed energy resources are capable of enhancing grid resilience through island operations in contingency. This paper proposes a multi-year, multi-criteria generation expansion model to achieve distribution power resilience through renewable energy integration. In cases that distribution circuits or fuel lifeline are destroyed post natural disasters, wind-and solar-based generators form island microgrids to power the critical load. The goal is to determine the sizing, siting and maintenance of distributed energy resources such that system cost and power shortage in contingency are minimized. Moment methods and central limit theorem are used to characterize the spatial climate uncertainty, generation intermittency, and voltage variation. The research contributions are twofold. First, the expansion model achieves triple goals by meeting the annual load growth, reducing the carbon footprint, and enhancing the grid resilience manifested as prevention and survivability. Second, we present an improved direct zigzag algorithm to search for the Pareto solutions. Numerical examples show that the zigzag search outperforms evolutionary-based algorithms as it can identify higher-quality non-dominant solutions by exploring a wider Pareto frontier.

A review of power system resilience assessment and enhancement approaches by focusing on wind farms and wind turbines

“…• an advanced model predictive control (MPC) to control the distributed energy resources (DER), minimize the impact of transient disruptions and speed up the response, and recovery time of the system [74] • a control between demand and supply by using ramp rate data of wind [48] • coordinated control between DER to manage of the demand and supply sides by the contribution of distribution system operator [82] • an optimizing method to control the islanded MG based on the multi-agent deep reinforcement learning by management of RES and load curtailment [17] • a bi-level method based on contingencies of the active distribution network against the wind storm, 1 st level: minimize the total cost, and 2 nd level: extracts the worst-case realization of the uncertainties to quickly start micro turbine (MT) and ESS after windstorm [13] • a controlling scheme of power flow according to demand and supply management to have a coordinated control in a DC highway [83] • coordinated control of resources and hourly network reconfiguration [81] • post-catastrophe system reconfiguration for distributed system [21] • CVR to control the power demand [24] • using DGs in islanded MG to keep the critical loads alive [84] • Coordination of WT allocation and network reconfiguration improve the performance of WT in islanded mode [80] • prioritizing the recovery of critical loads through the consideration of customer interruption cost [71] Planning Before an event, PA…”

“…In other words, the operator must be able to perform a coordinated resource control. In the case of WT(s) lost, a major problem that TABLE 6 Proposed methods to solve the resilience problems in the literatures Enhancement and restoration a heuristic method known as collective decision optimization algorithm is presented to solve complex and nonlinear resilience problems [59] Pareto optimal solutions to make a flexible decision [56] proposing an improved direct zigzag algorithm for Pareto solutions and critical load feeding [84] optimization the wind energy uncertainties by using data-based optimization [15] proposing a restoration optimization model with respect to deployment of repair crews based on complex network theory [57] Assessment a time series analysis model to evaluate the impact of wind storms on the power system [61] a general probabilistic framework of offshore WFs which are modeled as system-of-systems by a hierarchical model [90] using optimal power flow (OPF) and MC sequential simulation to evaluate the transmission line resilience [60] proposing a defender-attacker-defender method for harden the transmission systems by using the N-K contingency developed for any component failure and based on the random disruption and uncertainties of generation [15] a stochastic mixed-integer conic programming model for preparatory of DGs and ESSs before typhoon and decisions after that [71] sequential MC simulation and the DC OPF to calculate the proposed resilience index for a transmission network in [19] Uncertainty scenario Production Latin hypercube sampling method [7,51] combinatorial enumeration method [45] autoregressive integrated moving average method [24] normal distribution [17] MILP formulation [49,51] MC [15,21,25] Reduction mean relative closeness to other scenarios [21] Kantorovich distance [24,25,51] Backward method [25,86] Morris screening method and critical contingency identification [56] Multi-level soluti...…”

IoT-Based Mobile Energy Storage Operation in Multi-MG Power Distribution Systems to Enhance System Resiliency

“…where s is the solar irradiance and f b (s) is the beta distribution function. The beta distribution function parameters α and β can be calculated by Equation (4).…”

“…The authors in [3] propose a methodology for multi-MG adequate and stable operation to increase system resiliency during extreme events. The authors in reference [4] present a distributed generator-based expansion model in power distribution systems to achieve multiple goals, including annual demand increment, reducing emissions, and overall increasing system resiliency. The authors in reference [5] consider network nano grid rather than microgrid for electric vehicle battery operation using energy management for enhancing the system resiliency.…”

Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

“…Wang et al. determine the investment location, time and capacity of distributed energy to form a microgrid and enhance the recovery capacity of distribution network 8 …”