Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

Download Full-text

The cost of undisturbed landscapes: on the valuation of wind turbines in Austria

10.5194/egusphere-egu2020-11328 ◽

2020 ◽

Author(s):

Sebastian Wehrle ◽

Johannes Schmidt

Keyword(s):

Power System ◽

Wind Energy ◽

Wind Turbines ◽

Electricity Market ◽

House Prices ◽

Electricity Consumption ◽

Negative Externalities ◽

System Cost ◽

Electricity System ◽

Set Up

In Europe, the system cost minimizing highly renewable power system set-up predominantly relies on wind energy, with minor shares of photovoltaics.Yet, minimizing system cost neglects negative externalities of wind turbines, such as their impact on wildlife, noise emissions, landscape aesthetics, manifesting in local economic impacts such as a decline of house prices in the vicinity of wind turbines.To better understand the trade-off between electricity system cost and the negative externalities from wind turbines, we quantify the increase in electricity system cost when the system cost minimizing deployment of wind turbines is reduced in the favor of photovoltaics.Methodologically, we rely on the power system model medea, an open, techno-economic, numerical model of hourly dispatch and investment, set up to resemble the electricity market in Austria and its largest electricity trading partner Germany in 2030, when Austria aims to generate 90% of its electricity consumption from domestic renewable sources on annual balance.Depending on the capital cost of renewable energy technologies, the marginal system cost from displaced wind turbines can reach up to 40.000 EUR per MW and year or approximately 20 EUR per MWh. Moreover, CO2 emissions can increase by up to 1.2 million tons per year when wind energy is fully displaced. Producer surplus could increase by up to 220 million EUR per annum at intermediate wind energy displacement but falls back towards initial levels when wind energy is fully displaced.These numbers compare to estimates of property price declines between 2% and 16% caused by wind turbines, depending on the proximity to, and the visibility of the turbine. For illustration, adding a 3.5 MW wind turbine to a total installed wind power capacity of 12.6 GW in Austria over its lifetime (assuming a 3% discount rate) would generate sufficient social value to compensate affected property worth between 0.8 and 6.7 million EUR.

Download Full-text

Implementation of a Dynamic Network Model of the Nigerian Transmission Grid for Investigations on Power System Stability

10.31224/osf.io/r82zn ◽

2019 ◽

Author(s):

Kosisochukwu Pal Nnoli

Keyword(s):

Power System ◽

Dynamic Models ◽

Model Performance ◽

Dynamic Network ◽

Modern Society ◽

Electrical Energy ◽

Load Flow ◽

System Stability ◽

Power Network ◽

Transmission Grid

Electricity is the backbone of any modern society and economy. Therefore, economic growth and an increase in social wealth of a country usually lead to an increase in demand for electrical energy especially for a country as Nigeria. As the population of Nigeria is increasing exponentially, there exists a need to make basic needs constantly available, for the wellbeing of the increasing population. This is possible through mechanization. Reliable and stable electricity supply is the surest means to this end. As a result, there is a need to constantly review the dynamics of the power system while more energy sources and loads are being added to the existing power network grid. This creates a demand for precise models for the corresponding network. In this paper, the power network system of the Nigerian transmission grid was implemented at normal operations to include the dynamic models to the corresponding network elements (i.e. generation Units based on their installed capacities and controllers). With the help of this model, stationary load flow calculations, as well as the network’s model performance in steady state was conducted. The network’s model performance in the case of load changes and fault operations was also carried out. These allowed for investigations on the stability status of the Nigerian transmission grid.

Download Full-text

Research on Demand Side Response System of Electricity Price Under Electricity Market Incentive Mechanism

Distributed Generation & Alternative Energy Journal ◽

10.13052/dgaej2156-3306.3612 ◽

2021 ◽

Author(s):

Fu Xianyu ◽

Zhou Hongmei ◽

Qi-jie Jiang ◽

Ke Fan

Keyword(s):

Power System ◽

Demand Response ◽

Electricity Market ◽

Incentive Mechanism ◽

Operating Cost ◽

Electricity Price ◽

Demand Side ◽

Peak Shaving ◽

Optimal Dispatch ◽

Grid Operation

Aiming at the traditional day-ahead dispatching scheme of power generation, the paper proposes a power system security optimization dispatching model that considers the demand response of electricity prices under the electricity market incentive mechanism. Based on the peak and valley time-of-use electricity price, the paper establishes an incentive compensation mechanism to encourage users to be active. Participating in demand-side resource scheduling makes the effect of “peak shaving and valley filling” more pronounced. Simultaneously, to rationally configure the reserve capacity of grid operation, the system incorporates the expected power outage loss into the proposed model to ensure the grid operation safety. The analysis of calculation examples based on IEEE24 nodes shows that the power optimal dispatch model proposed in the paper considering demand response and expected outage loss can reduce the operating cost of the power grid under the premise of ensuring a certain level of reliability and realize the economy of the power system in the market environment and safe operation.

Download Full-text

A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing

Energies ◽

10.3390/en11123258 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3258 ◽

Cited By ~ 8

Author(s):

Feihu Hu ◽

Xuan Feng ◽

Hui Cao

Keyword(s):

Robust Optimization ◽

Demand Response ◽

Electricity Market ◽

Decision Model ◽

Expected Profit ◽

Optimization Approach ◽

Short Term ◽

Spot Prices ◽

Retail Prices ◽

Time Of Use Pricing

This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

Download Full-text

Multi-Agent Recommendation System for Electrical Energy Optimization and Cost Saving in Smart Homes

Energies ◽

10.3390/en12071317 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1317 ◽

Cited By ~ 7

Author(s):

Diego Jiménez-Bravo ◽

Javier Pérez-Marcos ◽

Daniel De la Iglesia ◽

Gabriel Villarrubia González ◽

Juan De Paz

Keyword(s):

European Union ◽

Recommendation System ◽

Energy Optimization ◽

Electricity Consumption ◽

Electrical Energy ◽

Energy System ◽

Optimization Method ◽

The European Union ◽

Electricity Prices ◽

Multi Agent

The European Union Establishes that for the next few years, a cleaner and more efficient energy system should be used. In order to achieve this, this work proposes an energy optimization method that facilitates the achievement of these objectives. Existing technologies allow us to create a system that optimizes the use of energy in homes and offers some type of benefit to its residents. Specifically, this study has developed a recommendation system based on a multiagent system that allows to obtain consumption data from electronic devices in a home, obtain information on electricity prices from the Internet, and provide recommendations based on consumption patterns of users and electricity prices. In this way, the system recommends new hours in which to use the appliances, offering the economic benefit that it would propose recommendations for the user. In this way, it is possible to distribute and optimize the use of energy in homes and reduce the peaks in electricity consumption. The system provides encouraging results in order to resolve the problem proposed by the European Union by optimizing the use of energy among different hours of the day and saving money for the customer.

Download Full-text

Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

Energies ◽

10.3390/en12142725 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2725 ◽

Cited By ~ 4

Author(s):

Alexandre Lucas ◽

Luca Jansen ◽

Nikoleta Andreadou ◽

Evangelos Kotsakis ◽

Marcelo Masera

Keyword(s):

Demand Response ◽

Electricity Market ◽

Market Integration ◽

Electricity Consumption ◽

Energy Transition ◽

Consumer Privacy ◽

Residential Sector ◽

Load Monitoring ◽

Load Disaggregation ◽

Aggregated Load

Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

Download Full-text

The chiller’s electricity consumption simulation by considering the demand response program in power system

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2018.12.121 ◽

2019 ◽

Vol 149 ◽

pp. 1114-1124 ◽

Cited By ~ 14

Author(s):

Zehui Shao ◽

Ehsan Gholamalizadeh ◽

Albert Boghosian ◽

Behnam Askarian ◽

Zhenling Liu

Keyword(s):

Power System ◽

Demand Response ◽

Electricity Consumption ◽

Demand Response Program

Download Full-text

Zero Emission Building And Conversion Factors Between Electricity Consumption And Emissions Of Greenhouse Gases In A Long Term Perspective

Environmental and Climate Technologies ◽

10.2478/rtuect-2014-0002 ◽

2014 ◽

Vol 13 (1) ◽

pp. 12-19 ◽

Cited By ~ 23

Author(s):

Ingeborg Graabak ◽

Bjørn Harald Bakken ◽

Nicolai Feilberg

Keyword(s):

Power System ◽

Electricity Market ◽

Large Scale ◽

Conversion Factor ◽

Electricity Consumption ◽

Life Time ◽

Market Model ◽

Conversion Factors ◽

Zero Emission

Abstract The CO2 emissions from a building’s power system will change over the life time of the building, and this need to be taken into account to verify whether a building is Zero Emission (ZEB) or not. This paper describes how conversion factors between electricity demand and emissions can be calculated for the European power system in a long term perspective through the application of a large scale electricity market model (EMPS). Examples of two types of factors are given: a conversion factor for average emissions per kWh for the whole European power system as well as a marginal factor for a specific region.

Download Full-text

Study on Load-Participated Demand Response Model Based on Load Aggregator

Frontiers in Energy Research ◽

10.3389/fenrg.2021.797979 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zixia Pei ◽

Yunlong Ma ◽

Mengyun Wu ◽

Jianlan Yang

Keyword(s):

Demand Response ◽

Electricity Market ◽

Operation Mode ◽

Electricity Consumption ◽

Research Direction ◽

Future Research ◽

Market Operation ◽

Residential Electricity ◽

Residential Electricity Consumption ◽

Essential Resource

In the reform of the electricity market, along with the gradual opening of the electricity sales side as well as the increase in the proportion of residential electricity consumption, the user load of the demand side has become an essential resource for demand response (DR). To efficiently utilize the residential load resources, new market participants, such as load aggregator (LA) have emerged. First, the basic concept of load aggregator is introduced in this paper, the origin and definition of LA is studied, and the classification of aggregated resources and the current situation of LA operation in some countries are presented. Then the article analyzes the market operation mode of LA and the uncertainty of LA in operation in detail, including the LA service on the user side, transaction mode and hierarchical structure associated with the operation, the uncertainty classification analysis, and associated strategies to address the problem. The LA load integration method and the scheduling control strategy are discussed. Finally, suggestions and ideas on the future research direction are proposed.

Download Full-text