Risk‐averse integrated demand response and dynamic G2V charge scheduling of an electric vehicle aggregator to support grid stability

Abstract

Electric vehicle aggregator (EVA) trades in energy and ancillary service markets for profit maximisation through grid-to-vehicle (G2V) charge scheduling of EVs. It can provide regulation services to system operator (SO) through coordinated and distributed charging of EVs. However, EV owner aim at minimum charging cost. Price-based demand response (PBDR) integrated charge scheduling can motivate EVs by off peak charging for reduced cost along with system load levelling. Real-time (RT) PBDR, adopted in literature, has low acceptance rate by EVs for being too dynamic to respond and being infeasible for few charge cycles in a day. Time of use (TOU) PBDR, being less dynamic, is a natural price signal for EVs and shields them from RTP volatility as well.Additionally, EV charging cost constrained EVA's charge scheduling is a realistic formulation and is subjected to multiple uncertainties like EVs' mobility behaviour and market prices. Considering this, risk-averse charge scheduling of EVA with TOU PBDR and EV charging cost constraint is proposed. EVs' mobility behaviour and market prices' uncertainties are modeled through stochastic programming. Agglomerative hierarchical clustering is proposed to develop TOU price for EVA from RTP. Conditional value at risk (CVaR) is used as risk measure. Results of a case study of EVA with 1000 EVs illustrate a List of symbols and abbreviations: N s , set of the scenarios with index s; T, set of the timeslots with index t (h); N, set of the electric vehicles with index i; P max i =P min i , upper/lower charging rate limit (kW) for EV i; BC i , the battery capacity of i th EV (kWh); Ef i , charging efficiency of EV i; TDC, the deliverable capacity of distribution transformer (MW); M t , markup pricing

Demand Response Management via Real-Time Pricing for Microgrid with Electric Vehicles under Cyber-Attack

“…The utility function U k Load,i (x k Load,i , ω k Load,i ) in microeconomics is used to reflect the satisfaction degree of user i who consumes x k Load,i energy in time slot k. Herein, the elastic coefficient which describes the flexibility of electricity consumption is positive, i.e., ω k Load,i > 0. In this study, a nondecreasing piecewise function is selected as the utility function [18][19][20][21].…”

“…Generally speaking, according to the actual situation of the energy system, we usually choose the following quadratic function as the cost function for EPs [18][19][20][21]:…”

“…In fact, prices based on DR mechanisms can better mobilize users' enthusiasm and participation in power grid operation. In [18], a charging scheduling problem of EVs based on time-of-use (TOU) price and considering the charging cost constraint was studied and a model was constructed. In [19], through studying the factors involved in the daily operation of the market, an optimal bidding strategy for EV aggregators was proposed.…”

Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management

“…The work in [106] also applies the combination of two types of demand response for EVA, which avoids the limits of choosing a single type of DR. Incentive-based demand response is used to improve the total effect of demand response, while the price-based demand response forces unwilling users to participate in the program. EVA is the entity that provides charging facilities to a group of EVs and acts as an intermediary between the distribution system operator and EVs owners to solve techno-economic problems in the operation and control of the electrical grid [107]. Aggregators must sign an agreement with EVs user indicating remuneration, the method of charging or discharging, limits on power production and reduction, etc.…”

Electric vehicle dispatching strategy considering time cost and risk of operating distribution network

“…To mitigate the risk posed to the security of power networks by a significant number of disorganised EV charging and discharging procedures, a risk constraint method for EVs was developed that took into account the power supply reliability of a photovoltaic (PV) system, and a risk analysis that takes into account each scenario's projected cost was suggested [9]. A scheduling strategy that considered the conditional value‐at‐risk as a risk measure and improved the income of EV aggregators by providing reserves to reduce the risk of safe operation of distribution networks was proposed [10]. In another study [11], a stochastic probability model of EVs was established, and a risk assessment method for a distribution network that considered the charging behaviour of EVs was proposed.…”