An evolutionary approach for the demand side management optimization in smart grid

Abstract

An important function of a Smart Grid (SG) is the Demand Side Management (DSM), which consists on controlling loads at customers side, aiming to operate the system with major efficiency and sustainability. The main advantages of this technique are (i) the decrease of demand curve's peak, that results on smoother load profile and (ii) the reduction of both operational costs and the requirement of new investments in the system. The customer can save money by using loads on schedules with lower taxes instead of schedules with higher taxes. In this context, this work proposes a simple metaheuristic to solve the problem of DSM on smart grid. The suggested approach is based on the concept of day-ahead load shifting, which implies on the exchange of the use schedules planned for the next day and aims to obtain the lowest possible cost of energy. The demand management is modeled as an optimization problem whose solution is obtained by using an Evolutionary Algorithm (EA). The experimental tests are carried out considering a smart grid with three distinct demand areas, the first with residential clients, other one with commercial clients and a third one with industrial clients, all of them possessing a major number of controllable loads of diverse types. The obtained results were significant in all three areas, pointing substantial cost reductions for the customers, mainly on the industrial area.

A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid

“…Maximizes the consumers' payoff by improving the economic efficiency of the residential consumption [77] Maximizes the welfare of consumers [89] Maximizes consumers profit [93] PAPR reduces [95] Minimizes the operation cost [98] Reduces the consumers' electricity expenses [112] Cost and PAPR reduction Minimizes consumers' bills by up to 25% [76] Electricity bill reduction Minimizes the waiting time of the appliances [113] Minimizes consumers' electricity consumption [81] The total generation cost is minimized at the Nash equilibrium point [86] Reduces the bills by up to 22% [91] Minimizes the cost of energy [87] Electricity cost reduction Minimizes the total average cost of electricity of all consumers [114] Electricity cost reduction and stabilization of the load profile Minimizes electricity consumption of all consumers [115] Generation cost and PAPR minimization Minimizes the consumers' energy costs [62] Maximization of the overall utility Satisfies the budget limits [80] Maximization of the welfare of consumers Minimizes transmission losses [96] Maximization of the generation Maximizes the generation capacity [116] Maximization of the social welfare Minimizes the electricity costs which, in turn, maximizes the welfare [117] Maximization of the consumer profit Minimizes the consumers' electricity bills [118] Minimization of the generation cost Reduces the generation cost and demand during peak hours which flattens the demanded load profile [92] Minimization of the generation cost and demand during peak hours…”

“…If the consumers agree with the schedular contracts, then they are rewarded in two different ways, i.e., either the aggregator will serve the consumers with electricity consumption at a lower price or they will be awarded an extra amount of energy during off-peak hours. An evolutionary algorithm based on day ahead DSM was proposed by the author of [118]. The objective of the proposed framework is to shave the demand during peak hours and to minimize the generation cost.…”

Day‐ahead demand side management using symbiotic organisms search algorithm

“…The target curve has been designed in (3) such that it is inversely proportional to the electricity market prices so that utility bill may be reduced [27] Here, is the average of the prices during the period, is the maximum price of the period and is the forecasted load consumption at time t . The data for hourly forecasted load and wholesale price are given in Table 1.…”

Gestión de la demanda en las redes inteligentes. Perspectiva y control desde el usuario y la distribuidora

“…Los siguientes artículos revisados utilizan métodos metaheurísticos: 1) En Vidal et al (2014) se optó por un algoritmo evolutivo al tener que trabajar con varios tipos de cargas de características de consumo diferentes propagadas a través del tiempo. Debido a la naturaleza de las cargas los autores consideraron que la programación lineal o la dinámica no eran apropiadas, además el algoritmo evolutivo permite cierta flexibilidad en el modelo y la priorización de cargas.…”