Optimización multiobjetivo del consumo de energía eléctrica basado en la programación de la demanda

This document presents a program of response to the electrical demand that allows prioritizing that electrical loads must be reduced or maintained to achieve energy savings through the mechanism of rate of prices of electricity in real time, this program is applied a Multi-Objective optimization that allows to include aspects such as the operability and times associated with the operation of an industrial process, for this case is the start time of the industrial processes the restriction that is added to achieve a complete behavior of the program, the optimization seek by means of the restrictions of energy get a management of the loads associated with the process, it is also shown how to reduce the energy consumption associated with the use of certain processes.En este documento se presenta un programa de respuesta a la demanda eléctrica que permita priorizar que cargas eléctricas deben ser reducidas o mantenerse para lograr un ahorro energético mediante el mecanismo de tarifa de precios de energía eléctrica en tiempo real , a este programa se le aplica una optimización multiobjetivo que permite incluir aspectos como la operatividad y los tiempos asociados a la operación de un proceso industrial, para este caso es el tiempo de arranque de los procesos industriales la restricción que se añade para lograr un comportamiento completo del programa, la optimización busca mediante las restricciones de tipo energético conseguir una gestión de las cargas asociadas al proceso, se muestra también como se logra reducir el consumo de energía asociados al uso de ciertos procesos

Impacts of Strategic Behavior and Consumer Requirements on the Promise of Demand Response

Demand response (DR) is envisaged to be of significance for enhancing the flexibility of power systems. The distributed nature of demand-side resources necessitates the need of an aggregator to represent the flexible demand in the electricity market. This paper presents a bilevel optimization model considering the optimal operation of a strategic aggregator in a day-ahead electricity market. Additionally, consumers’ requirements in terms of comfort satisfaction and cost reduction are considered by integrating detailed demand models and retail contract constraints. The results on the considered test system reveal that centralized optimization models would tend to over-estimate the capabilities of DR in an electricity market with strategic participants. Also, the flexibility value of DR for the power system and the profitability of the aggregator are significantly dependent on the retail contracts between the aggregator and the consumers, highlighting the need for careful contract design

Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach

In this paper, the problem of energy trading between smart grid prosumers, who can simultaneously consume and produce energy, and a grid power company is studied. The problem is formulated as a single-leader, multiple-follower Stackelberg game between the power company and multiple prosumers. In this game, the power company acts as a leader who determines the pricing strategy that maximizes its profits, while the prosumers act as followers who react by choosing the amount of energy to buy or sell so as to optimize their current and future profits. The proposed game accounts for each prosumer's subjective decision when faced with the uncertainty of profits, induced by the random future price. In particular, the framing effect, from the framework of prospect theory (PT), is used to account for each prosumer's valuation of its gains and losses with respect to an individual utility reference point. The reference point changes between prosumers and stems from their past experience and future aspirations of profits. The followers' noncooperative game is shown to admit a unique pure-strategy Nash equilibrium (NE) under classical game theory (CGT) which is obtained using a fully distributed algorithm. The results are extended to account for the case of PT using algorithmic solutions that can achieve an NE under certain conditions. Simulation results show that the total grid load varies significantly with the prosumers' reference point and their loss-aversion level. In addition, it is shown that the power company's profits considerably decrease when it fails to account for the prosumers' subjective perceptions under PT

Optimal management of demand response aggregators considering customers' preferences within distribution networks

In this paper, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by Distribution Platform Optimizer (DPO). This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs’ and customers’ preferences. A bilevel programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system, which consists of DRAs’ cost and other electricity trading costs, is minimized. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and MonteCarlo Simulation method. In the lower-level problem, the electricity bill for customers is minimized for customers. The income from DR selling is maximized based on DR prices through secure communication of household energy management systems (HEMS) and DRA. To solve this convex and continuous bilevel problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.©2020 The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Joint Optimal Pricing and Electrical Efficiency Enforcement for Rational Agents in Micro Grids

In electrical distribution grids, the constantly increasing number of power generation devices based on renewables demands a transition from a centralized to a distributed generation paradigm. In fact, power injection from Distributed Energy Resources (DERs) can be selectively controlled to achieve other objectives beyond supporting loads, such as the minimization of the power losses along the distribution lines and the subsequent increase of the grid hosting capacity. However, these technical achievements are only possible if alongside electrical optimization schemes, a suitable market model is set up to promote cooperation from the end users. In contrast with the existing literature, where energy trading and electrical optimization of the grid are often treated separately or the trading strategy is tailored to a specific electrical optimization objective, in this work we consider their joint optimization. Specifically, we present a multi-objective optimization problem accounting for energy trading, where: 1) DERs try to maximize their profit, resulting from selling their surplus energy, 2) the loads try to minimize their expense, and 3) the main power supplier aims at maximizing the electrical grid efficiency through a suitable discount policy. This optimization problem is proved to be non convex, and an equivalent convex formulation is derived. Centralized solutions are discussed first, and are subsequently distributed. Numerical results to demonstrate the effectiveness of the so obtained optimal policies are then presented