An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm

Abstract

a b s t r a c tIn this paper, a new approach is presented for developing optimal double-sided bidding strategy in security-constrained electricity markets by considering emission of pollutants, as further objectives. In the proposed methodology, both Generation Companies (GenCos) and Distribution Companies (DisCos) try to maximize their profit by implementation of optimal strategies, whiles they have incomplete information about their rivals and market mechanism of payment is locational marginal pricing. In addition, each participant provides its strategic bids based on supply function equilibrium model and it modifies its bidding strategies until Nash equilibrium points are computed. The proposed approach is modeled as a bi-level optimization problem with the upper sub problem addressing individual GenCos and DisCos and the lower sub problem addressing the Independent System Operator (ISO). The upper level maximizes the individual market participant's profit and the lower one solves the ISO's market clearing problem for maximizing Community Welfare Function (CWF). A Self-adaptive Global-based Harmony Search Algorithm (SGHSA) is used to obtain optimal bidding strategies. The proposed methodology has been implemented on the 6-machine 8-bus system test system to demonstrate the feasibility and effectiveness of the proposed approach. Simulation results illustrate the profitableness of the newly developed approach in the obtaining optimal bidding strategies.

Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling

“…[4]), the proposed GDCAC approach is implemented on a double-side day-ahead electricity market test system containing six GenCOs and five DisCOs [20], but without taking network constraints into consideration [9]. Considering that in a newly established electricity market, all participants are initially short of bidding experience and historical market data, they must firstly go through a repeated process of exploration and trial and error, accumulating experiences gradually, and then reach making more rational bidding decisions in the face of any market environment state.…”

“…Shivaie et al [20] proposed an environmental/techno-economic game approach for bidding strategies of GenCOs and DisCOs in a security-constrained day-ahead electricity market, and the dynamic process of bidding adjustment was simulated by a bi-level harmony search algorithm. In Reference [20], every GenCO and DisCO was assumed to have imperfect information about ongoing strategies of its rivals, but complete information about historical ones so that the parameters in optimization model of every GenCO or DisCO were estimated as the historical strategies of its rivals. In the study by Menniti et al [21], an evolutionary game model only to simulate the behaviors of the generation-side was proposed, and the modeling approaches of this paper can also be extended to the consumption-side issue.…”

“…Taking provisions (1)-(4) into consideration, the methods both in [8][9][10][11][12][13][14][15][16][17][18][20][21][22][23] and in [19,[24][25][26][27][28][29][30][31][32][33] are not quite suitable for modeling and simulating the practical day-ahead electricity market exactly, the reason of which is that in the real day-ahead electricity market, every participant can adjust its bidding strategy within a continuous interval of values, but the literatures suitable for provisions (1)- (3) have assumed that the sets for alternative actions (e.g., bidding strategies) or potential states (e.g., historical MCP) are discrete.…”

“…Taking [8,12,13,[20][21][22] as references, we only consider one (negotiation) time interval for the next day. Hence, maximizing Equations (5) and (6) are the objectives of GenCO i and DisCO j in the double-side day-ahead electricity market, respectively.…”

“…The parameters of GenCOs' and DisCOs' bid functions are shown in Table 1 [20]. It is assumed that 3] , (actually, changing the value interval of any strategy (k gi or k dj ) will not affect the final results of the Nash equilibrium).…”