A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market

Dotoli, Mariagrazia; Falagario, Marco; Sciancalepore, Fabio; Costantino, Nicola

doi:10.1016/j.compind.2014.02.014

Cited by 16 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Differently from MGP and MI, MSD is a pay-as-bid market, where the Transmission System Operator (TSO) procures the necessary resources for operating and monitoring the power system [9]. MSD consists of two submarkets: a scheduling phase ("MSD ex-ante") and a balancing market (MB) both aimed at relief the intrazonal congestions, create the energy reserves and ensure the real-time balance between generation and demand.…”

Section: Italian Electricity Marketmentioning

confidence: 99%

A Novel Operating Strategy for Customer-Side Energy Storages in Presence of Dynamic Electricity Prices

et al. 2015

View full text Add to dashboard Cite

In the wholesale energy market, electricity prices are determined by the balance between supply and demand. Normally, customers are not exposed to these variations but pay a constant electricity price. In an attempt to reduce demand peaks, several utilities are moving from a conventional fixed-rate pricing scheme to a new market-based model, based on time-of-use or real-time pricing, able to closely reflect the wholesale energy price. Electricity customers can thus take profit from the installation of storage systems, shifting their energy consumption from on-peak to off-peak periods. This paper presents a novel charging strategy to manage customer storage systems in presence of hourly electricity prices. The optimal operating schedule of the storage device is obtained by maximizing an objective function which corresponds to the maximum benefit for the storage owner. The proposed method is developed under the assumption that the operating scheduling of the battery energy storage system (BESS) does not depend on the specific facility's consumption. The model can be applied to several kinds of storages although the simulations refer to a lead-acid battery. operation strategy is effective to maximize the profit for the customer. The results also show that the investment can be recovered after several years when a lead-acid battery is used, but the situation will change in the near future, thanks to the cost reduction of storage technologies expected in coming years.

show abstract

Section: Italian Electricity Marketmentioning

confidence: 99%

A Novel Operating Strategy for Customer-Side Energy Storages in Presence of Dynamic Electricity Prices

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Taking into account in (23) the pricing models given in (21), (22), and (9), and considering the relationships of the energy flows from each system component in (5), (6), (11), and (18), the scheduling problem may be reformulated as in Table I. Naturally, the Table considers the aforementioned bounding constraints (1), (7), (13), (14), (19), inequality constraints (8), (16), (20), and equality constrains (2), (3), (15).…”

Section: G Formulation Of the Energy Scheduling Problemmentioning

confidence: 99%

Energy scheduling of a smart home under nonlinear pricing

Carli

Dotoli

2014

53rd IEEE Conference on Decision and Control

Self Cite

View full text Add to dashboard Cite

The paper focuses on the scheduling of energy activities in smart homes equipped with controllable electrical appliances, renewable energy sources, dispatchable energy generators, and energy storage systems. We formulate a mixed integer quadratic programming energy scheduling algorithm for cost minimization under nonlinear pricing. The scheduling technique manages the use of electrical appliances, plans the energy production and supplying, and programs the storage systems charging/discharging. A case study simulated in different scenarios demonstrates that the approach allows full exploitation of the potential of local energy generation and storage to reduce the individual user energy consumption costs, while complying with the customer energy needs. I. INTRODUCTION A. ContributionConcerns on energy consumption, both in terms of sustainability and resource exhaustion, are leading towards efficient energy behaviors all smart cities actors, including small end-users such as homes [3]. From its initial focus on residential comfort, the smart home concept is now focusing on the development and installation of new technologies and of effective and efficient energy management systems [11,16]. Hence, smart homes are proactive customers (prosumers) that negotiate and collaborate as an intelligent network in close interaction with their external environment [12]. The emergence of dynamic pricing policies [5], the diffusion of new devices enabling end-users to shift loads, produce, and store energy, and the new ability to access in real-time information on electricity consumption make the development of energy optimization algorithms a necessary condition to obtain responsive and proficient behaviors of domestic energy users [8].This paper proposes a scheduling technique of electrical activities in a smart home for energy costs minimization. The approach relies on a mixed-integer quadratic programming optimization model taking into account a novel realistic nonlinear pricing of energy and formulating a cost-optimal planning of the use of controllable appliances, the energy production from renewable energy generators, the energy supply from utilities, and the charging/discharging of storage devices, in the presence of innovative dispatchable R. Carli is with the

show abstract

“…Game theory encompasses a set of concepts such as the Nash equilibrium (NE) and collusive strategy (CS) with the purpose of taking the best decisions in gaming environments. In equilibrium, each player chooses an optimal strategy considering the other player's decisions . That is, in NE, any player cannot receive a better profit by unilateral changing of its action ; thus, a decision‐making player has no reason to deviate from NE .…”

Section: Introductionmentioning

confidence: 99%

An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

Mohtavipour

Zideh

2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

The aim of this article is to present a method, which is called the ‘iterative collusive strategy (CS) search method’, to detect the CS in prisoner's dilemma game in which there is collusive equilibrium. We apply this method to an example of two‐player prisoner's dilemma game and a numerical duopoly example to show its effectiveness. To simulate the electricity market models, we use this method with a local optimization algorithm. Then, we employ a hybrid technique by applying an agent‐based model to the iterative CS search method for improving the results and speeding up their convergence. We simulate an electricity market example with transmission constraint to test the effectiveness of local and hybrid iterative CS search methods. Simulation results show that both local and hybrid iterative CS search methods could successfully identify the CS; however, the hybrid iterative CS search method converges to the final results with less iteration numbers. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market

Cited by 16 publications

References 38 publications

A Novel Operating Strategy for Customer-Side Energy Storages in Presence of Dynamic Electricity Prices

A Novel Operating Strategy for Customer-Side Energy Storages in Presence of Dynamic Electricity Prices

Energy scheduling of a smart home under nonlinear pricing

An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

Contact Info

Product

Resources

About