Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation

Abstract

This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.

Sub-Transmission Network Expansion Planning Considering Regional Energy Systems: A Bi-Level Approach

“…Some studies have only considered traditional expansion planning with or without considering distributed generation (DG) units [4][5][6][7][8][9], and others have focused on the expansion of aggregated systems regardless of the expansion of the generation and transmission networks [10,11]. However, an integration of different sectors, including power generation firms, service providers, transmission firms, and aggregated systems can be taken into account in restructured power systems [12][13][14][15][16]. The authors in [16] proposed an approach that investigates the utilization of an aggregate system in the network and addresses the effect of the system on generation/transmission expansion planning (GEP/TEP) considering uncertainties.…”

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

“…They have concluded that the case with stochastic modeling has provided accuracy in scheduling and has decreased the PMG's OC. In, 31 the risk‐constraint stochastic modeling was introduced for a typical PMG. Different uncertainties such as WT generation and electricity prices were considered as independent uncertainties and electric vehicles and demand load as correlated uncertainties.…”

“…The most important contributions of this paper could be summarized as follows: A new probabilistic scenario‐based cost function for PMGs' optimal operation and scheduling is proposed. Different scenario reduction methods, for example, k‐means and k‐medoids algorithms as classical clustering ones and differential evolution algorithm (DEA) as a metaheuristic one, are comparatively applied, which has not been approached in recent studies 28‐31 Test results of the introduced method and MCS are compared to validate the presented method.…”

Cross‐regional integrated energy system scheduling optimization model considering conditional value at risk

“…In terms of operation management and scheduling of energy systems, an ontology‐driven multi‐agent based energy management system is proposed for monitoring and optimal control of an integrated homes/buildings and microgrid system, 14,15 an efficient strategy is developed to control local operation of each microgrid including the amount of energy to be requested from the main grid and the optimal charging/discharging patterns of batteries along with the transferred power among microgrids considering system's technical constraints, 16 a novel multi‐objective bidding strategy framework is proposed for a wind‐thermal photovoltaic system in the deregulated electricity market, 17 an optimization framework is developed for optimal operation of a CHP system in an uncertain environment considering demand response actions, 18 a conventional waste‐driven combined heat and power cycle is combined with a large‐scale absorption chiller to not only create a strong yet reliable synergy between the three energy sectors of cold, heat, and power, but also to improve the plant performance in terms of energy and sustainability indices 19 . In terms of risk research, a risk‐constrained scheduling optimization model is presented for a grid‐connected hybrid microgrid including demand response, electric vehicles, variable wind power generation, and dispatchable generation units, 20 a hierarchical stochastic energy management system is proposed for operation management of interconnected microgrids, 21 a novel stochastic model is presented from a microgrid operator perspective for energy and reserve scheduling considering risk management strategy 22 . In cross‐region integrated energy system research, energy hubs are created with the aim of considering the interactions between multi‐carrier energy systems throughout the smart grids 23 …”