Influence of Price Responsive Demand Shifting Bidding on Congestion and LMP in Pool-Based Day-Ahead Electricity Markets

Abstract

This paper investigates the influence of price responsive demand shifting bidding on congestion and locational marginal prices in pool-based day-ahead electricity markets. The market dispatch problem of the pool-based day-ahead electricity market is formulated as to maximize the social welfare of market participants subject to operational constraints given by real and reactive power balance equations, and security constraints in the form of apparent power flow limits over the congested lines. The social welfare objective function of the day-ahead market dispatch problem maximizes the benefit of distribution companies and other bulk consumers based on their price responsive demand shifting bids and minimizes the real and reactive power generation cost of generation companies. The price responsive demand shifting bidding mechanism, which has been recently introduced in the literature, is able to shift the price responsive demand from the periods of high price to the periods of low price in day-ahead electricity markets. The comparisons of the price responsive demand shifting bids with conventional price responsive and price taking bids are presented by solving hourly market dispatch problems on five-bus, IEEE 30-bus, realistic UP 75-bus Indian, and IEEE 118-bus systems for 24-h scheduling period. It has been demonstrated that the proposed approach leads to reduction in congestion and locational marginal prices as compared to price responsive and price taking bids and meets the energy consumption targets of distribution companies/bulk consumers.Index Terms-Congestion management, locational marginal price, price responsive demand shifting bidding, social welfare.

Demand Shifting Bidding in a Hybrid System with Volatile Wind Power Generation

“…The scope of present paper is to study the influence of PRDS bidding in a hybrid system with volatile Wind Power Generation in day-ahead markets.A usual PRDS bid is shown in [10]. Main dissimilarity between PRDS bidding scheme and price responsive demand bidding is that in PRDS bidding during a particular th t period, maximum demand limit can be greater than earlier, to include the fall of load occurred during peak periods due to soaring electricity rate, whereas in price responsive bidding scheme loss of load during peak hours can not recovered in off peak hours.…”

“…For a complete and compact formulation of the PRDS bidding mechanism the reader is referred to [9], [10]. In the optimization program used for market-clearing, the bid specifications are translated into constraints: (i) on the demand during each period and (ii) on the total demand over the optimization horizon.…”

“…However, the market clearing mechanisms developed in [9] do not take into account the operational and security constraints of transmission networks. Kanwardeep Singh et al [10] discussed the influence of PRDS bidding on congestion and LMP in Pool-Based Electricity Markets. Impacts of availability based tariff on wind power trading option were analyzed in [11].…”

Demand Response and Economic Dispatch of Power Systems Considering Large-Scale Plug-in Hybrid Electric Vehicles/Electric Vehicles (PHEVs/EVs): A Review

“…Demand bidding programs require customers to bid for certain load reductions in an electricity wholesale market. When a bid is accepted, customers should curtail their load by the amount specified in the bid or face a penalty [40]. PHEVs/EVs are valuable resources for demand response, and their importance cannot be overlooked in future demand side management (DSM).…”

Economic optimization of smart distribution networks considering real-time pricing

“…However, the structure of distribution networks currently is becoming more complex and cannot be modeled as a passive node because of the demand response and distributed generators (DGs) [12]. Furthermore, distribution networks transport electricity from DGs instead of transmission systems to end customers, which not only reduce distribution network usage cost but also potentially avoid congestion in transmission systems [13][14][15][16][17][18][19][20][21][22]. In [15], the optimal objectives are maximizing the power usage from DGs and the power usage costs of distribution networks.…”