Smart energy pricing for demand‐side management in renewable energy smart grids

• Author: Fabrizio Granelli,Rafael C. S. Schouery,Flávio K. Miyazawa,Nelson L. S. da Fonseca,Lucas P. Melo,Yulle G. F. Borges
• Date: 01 November 2020
• DOI: http://doi.org/10.1111/itor.12747
• Published date: 01 November 2020

Intl. Trans. in Op. Res. 27 (2020) 2760–2784

DOI: 10.1111/itor.12747

INTERNATIONAL

TRANSACTIONS

IN OPERATIONAL

RESEARCH

Smart energy pricing for demand-side management

in renewable energy smart grids

Yulle G. F. Borgesa,∗, Rafael C. S. Schouerya,Fl

´

avio K. Miyazawaa,

Fabrizio Granellib, Nelson L. S. da Fonsecaaand Lucas P. Meloa

aInstitute of Computing, University of Campinas, Av. Albert Einstein 1251, Campinas 13083-852, Brazil

bDepartment of Information Engineering and Computer Science, University of Trento, Via Sommarive,9, Povo I-38123,

Italy

E-mail: glebbyo@ic.unicamp.br [Borges];rafael@ic.unicamp.br [Schouery]; fkm@ic.unicamp.br [Miyazawa];

fabrizio.granelli@unitn.it [Granelli];nfonseca@ic.unicamp.br [da Fonseca]; lucas.melo@ic.unicamp.br [Melo]

Received 30 September 2018; receivedin revised form 9 October 2019; accepted 16 October 2019

Abstract

Smart grids are expected to provide various benefits to society by integrating advances in power engineering

with recent developments in the field of information and communications technology. One of the advantages

is the support to efficient demand-side management (DSM), for example, changes in consumer demands for

energy based on using incentives. Indeed, DSM is expected to help grid operators balance time-varying gen-

eration by wind and solar units, and the optimization of their usage. This paper focuses on DSM considering

renewable energy generation and proposes an auction, in which consumers submit bids to renewable energy

usage plans. An additional model is introduced to allow consumers to compute their bid for a given usage

plan. Both models have been extendedto include energy storage devices. The proposed model is compared to

a system with time-varying pricing for energy, where it is shown to allow consumers to use more appliances,

to lead to a larger profit, and to reduce the peak-to-average ratio of energy consumption. Finally, the impact

of the use of energy storage in households and in the energy provider is also considered.

Keywords:demand-side management; smart grid; smart pricing; auction; energy storage devices

1. Introduction

The European Technology and Innovation Platforms – Smart Networks for Energy Transition

defines smart grids as “electricity networks that can intelligently integrate the actions of all users

connected to it—generators, consumers, and those that do both—in order to efficiently deliver

sustainable, economic and secure electricity supplies” (European Technology & Innovation Plat-

forms, 2018). In a usual smart grid scenario, each consumer is equipped with energy consumption

∗Corresponding author.

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Y.G.F. Borges et al. / Intl. Trans. in Op. Res. 27 (2020) 2760–2784 2761

scheduling (ECS) devices that are able to automatically start, stop, increase, or decrease the energy

consumption of appliances, as well as smart metering equipment that allows the grid to gather

advanced information on energy consumption on the end users. Having such infrastructure, energy

providers can obtain more information on how and when the energy is consumed, thus charging

accordingly and leading the system to a better use overall instead of coping with peak demands.

Consumers also benefit from the infrastructure by making better scheduling for their appliance

usage while taking into consideration the changes in the energy cost set by the providers at a

certain time. However, this comes at a high cost of investments in information and communica-

tion technology, such as cognitive radio network infrastructures, smart sensing equipment (Shah

et al., 2013; Khan et al., 2016), and research on the efficient algorithmic implementation of such

systems.

Initiatives such as the Europe 2020 Climate and Energy Package—which aims to cut greenhouse

gas emissions by 20%, establish 20% of the EU’s energy from renewable sources, and show an

improvement of 20% in energy efficiency by 2020—have pushed for the implementation of smart

grids through legislation and incentives, indicating that they may, in fact, play a key role in the

transition into a more sustainable energy production, distribution, and consumption (European

Commission, 2008). As opposed to the current grid, smart grids take advantage of advanced

metering infrastructure, and supervisory control and data acquisition, while also being capable of

self-healing. There is a vast body of literature regarding smart grids, however this paper focuses

on the connection between game theory and demand-side management (DSM) in smart grids.

For an overview of the applications of game theory in smart grids, please refer to Saad et al.

(2012).

DSM is a set of techniques implemented by utility companies designed to influence the energy

consumption of their end users in order to achieve a more efficient grid operation in relation to

the available power plant capacity (Ng and Sheble, 1998). The main DSM techniques include load

management and demand response. Load management is usually implemented as directload control

where based on an agreement between the utility company and consumers, the utility company can

remotely control the operation of certain appliances of their consumers in order, for instance, to

avoidglobal peaks of usage. On the other hand, demand response is based on implementing financial

or other incentives to influence consumers’ demand for energy. One common way to implement

demand response is to use smart pricing, in which the utility company sets the price of the energy

according to the aggregated load of the consumers, encouraging their end users to shift their load

to off-peak hours.

In this paper, a smart grid is considered in which traditional power plants (carbon, nuclear,

etc.) are integrated with renewable power plants (solar panels, wind turbines, etc.) to provide

power to small communities of consumers. A novel smart pricing scheme is proposed to allocate

energy to consumers (households) on the basis of constraints imposed by energy production.

The proposed scheme charges consumers for an energy usage plan. As usual in auction theory,

consumers’ satisfaction with a usage plan is modeled as a monetary gain, which translates to an

auction bid for the respective usage plan. A separate model is also proposed to help consumers

to compute their value for a given energy usage plan based on a discrete model for describing

appliance usage. Plans are assigned to consumers in such a way that the renewable energy capacity

is not exceeded, yet maximizes the overall value of these assigned energy plans. The Vickrey–

Clarke–Groves (VCG) mechanism (Vickrey, 1961; Clarke, 1971; Groves, 1973) is used to incentivize

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2019 The Authors.

International Transactionsin Operational Research C

2019 International Federation of OperationalResearch Societies