Optimal Operation and Bidding Strategy of a Virtual Power Plant Integrated With Energy Storage Systems and Elasticity Demand Response

Abstract

As an aggregator involved in various renewable energy sources, energy storage systems, and loads, a virtual power plant (VPP) plays a key role as a prosumer. A VPP may enable itself to supply energy and ancillary services to the utility grid. This paper proposes a novel scheme for optimizing the operation and bidding strategy of VPPs. By scheduling the energy storage systems, demand response, and renewable energy sources, VPPs can join bidding markets to achieve maximum benefits. The potential uncertainties caused by renewable energy sources and the demand response are considered in a robust optimization model. Moreover, the robust VPP optimization accounts for its influence on markets to ensure optimal energy and reserve capacity bidding transactions in the day-ahead market and deals balancing in the real-time market. To demonstrate the performance of the proposed scheme, markets comprising various participants and managed by the system operator are implemented using mathematical models. The proposed method is evaluated using an illustrative system and the practical Taiwan power (Taipower) system with diverse uncertainty levels. The numerical results demonstrate the promising performance and the efficiency of the proposed method. The results also verify the effectiveness of the proposed method VPP with various combinations of renewable energy sources, energy storage systems, and loads. INDEX TERMSVirtual power plant, demand response model, ancillary service, energy storage system, electricity markets, renewable energy source, robust optimization, game theory, mixed integer programming.

Optimal Bidding of Price-Maker Retailers With Demand Price Quota Curves Under Price Uncertainty

“…where With multi-segment bidding rule, the optimal bidding model is formulated as (19). The constraints represent that the bidding price has to be not less than the floor price and not larger than the cap price set by the market.…”

“…A pricemaker refers to a facility, which is large enough that its action can alter market prices. Price-maker strategies are initially studied for generators, such as thermal generators [3][4][5][6][7][8][9][10][11][12], hydro generators [13], wind power [14], energy storage [15][16][17], and then applied to the demand side [18,19].…”

“…These methods simulate the market operation by several agents. For example, [19] presents a two-level game simulation for virtual power plant (VPP) to optimize its bidding in the day-ahead and real-time energy market. [8] applies Q-learning method to simulate an oligopolistic electricity market and study the effects of bidding behaviors of the generators.…”

Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration

“…The objective function (10) minimizes the system operating cost C, which consists of coal consumption cost a i (P i,t ) 2 + b i P i,t + c i , start-up cost C i,t , oil cost C oil i,t and pollution charges C ev i,t [42][43][44]. In the process of power system operation optimization, taking no account of the cost of wind power, the system operating cost is mainly the operating cost of thermal units which is primarily composed of coal consumption costs and start-up costs when thermal units provide basic peaking services [45]. However, the boiler combustion of thermal units is inefficient without firing oil when thermal units are operating in deep peaking status for providing deep peaking service, which will increase oil costs.…”

Optimal Economic Dispatch for an Industrial Park With Consideration of an Elastic Energy Cloud Model With Integrated Demand Response Uncertainty

“…Currently, the forms of energy use of integrated energy terminal MEUs are divided into rigid energy and elastic energy. Rigid energy is not subject to changes in some incentives, while elastic energy is the part of energy or time that can be adjusted by the stimulation of some factors [5][6]. Therefore, this paper establishes an elastic energy cloud model that comprehensively considers the uncertainty of MEU participation in IDR and then designs a set of economic scheduling strategies that consider the uncertainty and randomness of the elastic energy cloud model.…”