Integrated demand response based on household and photovoltaic load and oscillations effects

Cao, Weijun; Pan, Xiao; Sobhani, Behrouz

doi:10.1016/j.ijhydene.2021.08.212

Cited by 10 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various sensors inside and outside buildings and a unified system can control all welfare and security conditions in real-time. This way, energy losses are controlled and used to achieve an ideal condition [3]. Building smartization systems provide all these items for the residents to better exploit building spaces [4].…”

Section: Costmentioning

confidence: 99%

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Zhang¹,

Yang²,

Li³

2022

Energy Engineering

View full text Add to dashboard Cite

Buildings are the main energy consumers across the world, especially in urban communities. Building smartization, or the smartification of housing, therefore, is a major step towards energy grid smartization too. By controlling the energy consumption of lighting, heating, and cooling systems, energy consumption can be optimized. All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources (RES), which mitigates environmental impacts and reduces peak demand for electrical energy. In this paper, a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes. In this way, large power stations that work with fossil fuels will no longer be developed. The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles (EVs) with bidirectional power exchangeability with the power grid, an energy storage system (ESS), and solar panels. Additionally, the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work. Different case studies, including the sales of electrical energy resulting from PV panels' generated power to the power grid, time-variable loads such as washing machines, and different demand response (DR) strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs, BESS, and solar panels. The proposed model was simulated in MATLAB. A hybrid particle swarm optimization (PSO) and gravitational search (GS) algorithm were utilized for optimization. Scenario generation and reduction were performed via LHS and backward methods, respectively. Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use (STOU) loads, EV, ESS, and PV system. Based on the results, the proposed model markedly reduced the electricity costs of the smart house.

show abstract

Section: Costmentioning

confidence: 99%

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Zhang¹,

Yang²,

Li³

2022

Energy Engineering

View full text Add to dashboard Cite

show abstract

“…Among these factors, one can mention increased energy consumption, increased fossil fuel price, an acute rise in the costs of constricting new concentrated power plants, and environmental concerns and factors. The use of distributed energy sources can potentially reduce the need to develop of traditional systems [1]. Although controlling a large number of these sources would create a real challenge in safe and efficient grid operation, this challenge can be largely managed by the concept of microgrids.…”

Section: Introductionmentioning

confidence: 99%

Modeling Energy Consumption in the Production Processes of Industrial Units Based on Load Response Programs in the Energy Market

Li¹

2023

Energy Engineering

View full text Add to dashboard Cite

The optimal operation of microgrids is of great significance for the sake of efficient and economical management of its energy resources. The microgrid energy management system should plan to operate the microgrid while simultaneously considering the electric and thermal load. The present study proposes energy management to minimize the costs of operating an industrial microgrid. In fact, planning for energy supply is among the critical issues that distribution companies deal with daily in the competitive environment. A distribution company usually meets customer (end customer) demands by purchasing energy from a wholesale market. Given the load curtailment, distribution companies have more choices and interactions in the market. Distribution companies face the two uncertainties of load changes and price fluctuations in their daily energy supply planning which could lead to the risk of loss resulting from the distribution company's decision-making for daily energy supply planning. Thus, these companies face the challenge of maximizing profit in a risk-based environment. Therefore, the present study presents an optimal model of energy consumption in the production processes of aluminum and cement industrial units. The presented model was then used in planning the day-ahead energy of a microgrid containing these industrial units. Since the studied subject has many limitations, it would be difficult to solve it using mathematical methods. To resolve this issue then, the present study introduces a newly developed algorithm inspired by bee colonies. The proposed method seeks to significantly improve in the local and global search capabilities. In addition to confirming the validity of the proposed model, results indicate that the implementation of load-response programs and the cooperation of industrial units in the ancillary services and energy market can increase the profits of units and microgrids as well as correct the demand curve. According to the obtained results from the first and second test cases, the total profit of the aluminum unit

show abstract

“…The use of renewable energies, such as PV energy, is on the rise in households as a way to decrease energy costs [24][25][26]. Many EU countries have implemented policies to encourage the development of self-consumption of renewables, with a focus on PV.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Examination of the Electricity Saving Potentials of Direct Residential PV Energy Use in European Countries

et al. 2023

View full text Add to dashboard Cite

The increasing global penetration of photovoltaic (PV) technology creates not only enormous opportunities for clean energy production but also poses challenges that put energy systems to the test. Although there are many ways of dealing with the rising share of PV energy, most of these solutions require substantial funding, time, and effort to implement, which highlights the importance of solving some of the issues at their source, i.e., through the direct use of the electricity generated at PV power plants, many of which are owned and operated by households. In recent decades, PV technology has experienced an unprecedented growth in Europe due to a range of reasons, including the nations’ policies and supporting schemes. The goal of the present research was to determine the annual consumption of electricity per capita in the households of certain European countries and how much of this can be covered by the direct use of PV energy in the case of an on-grid PV system and to show what the annual potential of the direct use of PV energy is compared to the annual energy production of the PV systems. The significance and novelty of this research is justified by the lack of comparative scientific studies related to the annual potential of the direct household use of PV energy, which could alleviate some of the problems facing electricity networks with high shares of PV energy. The novel practical benefit of the study was determining, for the examined European countries, the extent to which direct household PV energy use could lower annual electricity consumption from the grid, in the case of on-grid PV systems of different capacities. In addition, these findings also provide information related to the grid’s macro-energy systems in terms of local network load effects related to given investment sizes.

show abstract

Integrated demand response based on household and photovoltaic load and oscillations effects

Cited by 10 publications

References 26 publications

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Modeling Energy Consumption in the Production Processes of Industrial Units Based on Load Response Programs in the Energy Market

A Comparative Examination of the Electricity Saving Potentials of Direct Residential PV Energy Use in European Countries

Contact Info

Product

Resources

About