scholarly journals Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 660
Author(s):  
Chunyu Deng ◽  
Kehe Wu
Keyword(s):  
Demand Response ◽  
Electricity Market ◽  
Power Distribution ◽  
Peak Load ◽  
Electricity Consumption ◽  
Response Strategy ◽  
Response Strategies ◽  
Policy Gradient ◽  
Incentive Policies ◽  
Residential Demand

With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market.

scholarly journals An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

2018 ◽  
Vol 30 (1) ◽  
pp. 63-80 ◽  
Author(s):  
Paraskevas Panagiotidis ◽  
Andrew Effraimis ◽  
George A Xydis
Keyword(s):  
Demand Response ◽  
Energy Demand ◽  
Moving Average ◽  
Electricity Consumption ◽  
End Users ◽  
Market Model ◽  
Residential Sector ◽  
Response Strategies ◽  
System Operator ◽  
Modelling Methodology

The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

scholarly journals North–south polarization of European electricity consumption under future warming

2017 ◽  
Vol 114 (38) ◽  
pp. E7910-E7918 ◽  
Author(s):  
Leonie Wenz ◽  
Anders Levermann ◽  
Maximilian Auffhammer
Keyword(s):  
Climate Change ◽  
Dose Response ◽  
Electricity Market ◽  
Western Europe ◽  
Peak Load ◽  
Electricity Consumption ◽  
Total Consumption ◽  
Electric Sector ◽  
Future Warming ◽  
The Impact

There is growing empirical evidence that anthropogenic climate change will substantially affect the electric sector. Impacts will stem both from the supply side—through the mitigation of greenhouse gases—and from the demand side—through adaptive responses to a changing environment. Here we provide evidence of a polarization of both peak load and overall electricity consumption under future warming for the world’s third-largest electricity market—the 35 countries of Europe. We statistically estimate country-level dose–response functions between daily peak/total electricity load and ambient temperature for the period 2006–2012. After removing the impact of nontemperature confounders and normalizing the residual load data for each country, we estimate a common dose–response function, which we use to compute national electricity loads for temperatures that lie outside each country’s currently observed temperature range. To this end, we impose end-of-century climate on today’s European economies following three different greenhouse-gas concentration trajectories, ranging from ambitious climate-change mitigation—in line with the Paris agreement—to unabated climate change. We find significant increases in average daily peak load and overall electricity consumption in southern and western Europe (∼3 to ∼7% for Portugal and Spain) and significant decreases in northern Europe (∼−6 to ∼−2% for Sweden and Norway). While the projected effect on European total consumption is nearly zero, the significant polarization and seasonal shifts in peak demand and consumption have important ramifications for the location of costly peak-generating capacity, transmission infrastructure, and the design of energy-efficiency policy and storage capacity.

The role of demand response in residential electricity load reduction using appliance shifting techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Timothy King Avordeh ◽  
Samuel Gyamfi ◽  
Alex Akwasi Opoku
Keyword(s):  
Demand Response ◽  
Air Conditioning ◽  
Electricity Consumption ◽  
Response Strategy ◽  
Load Reduction ◽  
Residential Homes ◽  
Load Shifting ◽  
Content Type ◽  
Peak Loads ◽  
Residential Electricity

Purpose Some of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa. Design/methodology/approach The research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior. Findings This study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions. Research limitations/implications It should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households. Practical/implications Load shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers. Originality/value This paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

scholarly journals Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

Energies ◽  
2016 ◽  
Vol 9 (9) ◽  
pp. 716 ◽  
Author(s):  
Maytham Ahmed ◽  
Azah Mohamed ◽  
Raad Homod ◽  
Hussain Shareef
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Response Strategy ◽  
Home Energy Management ◽  
Residential Demand

scholarly journals Mitigating Electricity a Price Spike under Pre-Cooling Method

2016 ◽  
Vol 6 (3) ◽  
pp. 1281
Author(s):  
Marwan Marwan ◽  
Pirman Pirman
Keyword(s):  
Electricity Market ◽  
Air Conditioning ◽  
Energy Savings ◽  
Peak Load ◽  
Electricity Consumption ◽  
Electrical Network ◽  
Cooling Method ◽  
Proposed Model ◽  
Substantial Risk ◽  
Electricity Utilities

The growing demand for air-conditioning is one of the largest contributors to Australia overall electricity consumption. This has started to create peak load supply problems for some electricity utilities particularly in Queensland. This research aimed to develop a consumer demand side response model to assist electricity consumers to mitigate peak demand on the electrical network. The proposed model allows consumers to independently and proactively manage air conditioning peak electricity demand. The main contribution of this research is how to show consumers can mitigate peak demands by optimizing energy costs for air conditioning in a several cases such as no spike and spike considering to the probability spike cases may only occur in the middle of the day for half hour, one hour and one and half hour spikes. This model also investigates how air conditioning applied a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve energy savings and reducing electricity bills (costs) to the consumer. The model was tested with the Queensland electricity market data from Australian Energy Market Operator and Brisbane temperature data from Bureau statistic during hot days.

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

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2725 ◽  
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.

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

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2867 ◽  
Author(s):  
Zhanle Wang ◽  
Raman Paranjape ◽  
Zhikun Chen ◽  
Kai Zeng
Keyword(s):  
Power System ◽  
Demand Response ◽  
Electricity Market ◽  
Energy Cost ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
System Stability ◽  
Optimization Approach ◽  
Model Case ◽  
Multi Agent

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.

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