Division of the energy market into zones in variable weather conditions using Locational Marginal Prices

Abstract

Adopting a zonal structure of electricity market requires specification of zones' borders. One of the approaches to identify zones is based on clustering of Locational Marginal Prices (LMP). The purpose of the paper is twofold: (i) we extend the LMP methodology by taking into account variable weather conditions and (ii) we point out some weaknesses of the method and suggest their potential solutions. The offered extension comprises simulations based on the Optimal Power Flow (OPF) algorithm and twofold clustering method. First, LMP are calculated by OPF for each of scenario representing different weather conditions. Second, hierarchical clustering based on Ward's criterion is used on each realization of the prices separately. Then, another clustering method, i.e. consensus clustering, is used to aggregate the results from all simulations and to find the global division into zones. The offered method of aggregation is not limited only to LMP methodology and is universal.

Decomposition of power flow used for optimizing zonal configurations of energy market

“…Once the influence of each node of the zone on the loop flow is determined by PF decomposition, hierarchic clustering [7] can be used to group nodes of the target zone. Our approach is to utilize values of power injections responsible for loop flows (and only themcf.…”

“…There are two main groups of methods capable of achieving that goal. The first is based on Locational Marginal Prices [5][6][7][8][9] and aims at aggregation of nodes characterized by similar cost of energy delivered to the node in the nodal model. Second class of algorithms aggregates nodes characterized by similar Power Transfer Distribution Factors in respect to overloaded lines [10][11][12].…”

Market coupling as the universal algorithm to assess zonal divisions

“…Hence, (i) the single-price equilibrium set on the market (energy exchange) is frequently unfeasible, (ii) the system operator has to perform costly readjustments, (iii) costs of supplying the energy differ between locations, but they are not covered where they arise. 1 With introduction of other forms of market, congestion costs are mitigated and the price on the market reflects the true costs of supplying energy to different locations in a more adequate way.…”

Decomposition of Power Flow Used for Optimizing Zonal Configurations of Energy Market

“…Once the influence of each node of the zone on the loop flow is determined by PF decomposition, hierarchic clustering [7] can be used to group nodes of the target zone. Our approach is to utilize values of power injections responsible for loop flows (and only them -cf.…”

“…There are two main groups of methods capable of achieving that goal. The first is based on Locational Marginal Prices [5][6][7][8][9] and aims at aggregation of nodes characterized by similar cost of energy delivered to the node in the nodal model. Second class of algorithms aggregates nodes characterized by similar Power Transfer Distribution Factors in respect to overloaded lines [10][11][12].…”