Reserve costs allocation model for energy and reserve market simulation

Abstract

Abstract-This paper proposes a new model to allocate reserve costs among the involved players, considering the characteristics of the several entities, and the particular circumstances at each moment. The proposed model is integrated in the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM), which enables complementing the multi-agent simulation of diverse electricity market models, by including the co-simulation of energy and reserve markets. In this context, the proposed model allows allocating the payment of reserve costs that result from the reserve market. A simulation based on real data from the Iberian electricity market -MIBEL, is presented. Simulation results show the advantages of the proposed model in sharing the reserve costs fairly and accordingly to the different circumstances. This work thus contributes the study of novel market models towards the evolution of power and energy systems, with the objective of adapting current and future models to the new paradigm of high renewable energy generation penetration.

The Uncertain Bidder Pays Principle and Its Implementation in a Simple Integrated Portfolio-Bidding Energy-Reserve Market Model

“…Regarding the allocation of reserve-related balancing and ramping costs, Reference [10] proposes a unit commitment-based approach, applying the principle of pareto-optimality for the problem. Reference [11] aims to distribute the reserve cost among the most appropriate consumers, applying agent-based modelling and simulation approach. A somewhat similar agent-based approach combined with stochastic unit commitment for the reserve cost allocation problem is presented in Reference [12].…”

“…While uncertainty characterization in the majority of previous literature was focussing to either power plants or customers, in the proposed approach we consider the potential uncertainty of both side of the energy market-both uncertain supply and demand energy bids are considered. Furthermore while previous methods use computationally demanding agent based modelling (as References [11,12]) or include quadratic constraints [10] (or their semidefinite relaxation), we formulate the suggested method as a simple mixed integer linear problem (MILP), which can be efficiently solved, for example, via Benders-decomposition [16] and/or the branch-and-bound algorithm [17]. (Both techniques are widely used to solve problems in the power sector [18][19][20].)…”

CTRANSPORT: Multi-agent-based simulation

“…Concept of agent was first proposed by Hewitt (Hewitt, 1977), and makes reference to a computational system that inhabits a complex environment and acts autonomously in order to achieve specific goals. M.A.S are composed by several agents that cooperate, coordinate, negotiate, and the like (Wooldridge, 2009, Pinto et al, 2017.…”

Multi-agent architecture for local electricity trading in power distribution systems

“…In this way, customers are able to express their desired reliability level to guarantee their desired electrical demand considering the uncertainty of the power distribution grid. The Demand Factor (DF)-which which has been introduced in [Gazafroudi et al, 2015], [Pinto et al, 2017], [Gazafroudi et al, 2017c] and [Gazafroudi et al, 2019b]-is modified in this work as represented in (5.13). Eqs.…”