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.

Energy demand projections and relevance of income dynamics in Gauteng’s residential sector

2011 ◽  
Vol 22 (4) ◽  
pp. 31-47 ◽  
Author(s):  
Mamahloko Senatla
Keyword(s):  
Energy Consumption ◽  
Low Income ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Planning System ◽  
Strategy Development ◽  
Computing Power ◽  
Residential Sector ◽  
Energy Modelling ◽  
Main Driver

Energy modelling serves as a crucial tool for informing both energy policy and strategy development. But the modelling process is faced with both sectoral energy data and structural challenges. Among all the sectors, the residential sector usually presents a huge challenge to the modelling profession due to the dynamic nature of the sector. The challenge is brought by the fact that each an every household in a region may have different energy consumption characteristics and the computing power of the available models cannot incorporate all the details of individual household characteristics. Even if there was enough computing power within the models, energy consumption is collected through surveys and as a result only a sample of a region is captured. These challenges have forced energy modellers to categorise households that have similar characteristics. Different researchers choose different methods for categorising the households. Some researchers choose to categorise households by location and climate, others choose housing types while others choose quintiles. Currently, there is no consensus on which categorisation method takes precedence over others. In these myriad ways of categorising households, the determining factor employed in each method is what is assumed to be the driver of energy demand in that particular area of study. Many researchers acknowledge that households’ income, preferences and access to certain fuels determine how households use energy. Although many researchers recognise that income is the main driver of energy demand in the residential sector, there has been no energy modelling study that has tried to categorise households by income in South Africa. This paper chose to categorise households by income because income is taken to be the main driver of energy demand in the urban residential sector. Gauteng province was chosen as a case study area for this paper. The Long-range Energy Alternatives Planning System (LEAP) is used as a tool for such analysis. This paper will further reveal how the dynamics of differing income across the residential sector affects total energy demand in the long run. The households in Gauteng are classified into three income categories – high, middle and low income households. In addition to different income categories, the paper further investigates the energy demand of Gauteng’s residential sector under three economic scenarios with five energy demand scenarios. The three economic scenarios are first economic scenario (ECO1), second economic scenario (ECO2) and third economic scenario (ECO3). The most distinguishing factor between these economic scenarios is the mobility of households from one income band to the next.The model results show that electricity demand will be high in all the three economic scenarios. The reason for such high electrical energy demand in all the economic scenarios compared to other fuels is due to the fact that among all the provinces, Gauteng households have one of the highest electricity consumption profiles. ECO2 showed the highest energy demand in all the five energy demand scenarios. This is due to the fact that the share of high income households in ECO2 was very high, compared to the other two economic scenarios. The favourable energy demand scenarios will be the Energy Efficiency and MEPS scenarios due to their ability to reduce more energy demand than other scenarios in all the three economic scenarios.

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 Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

2020 ◽  
Vol 10 (2) ◽  
pp. 594
Author(s):  
Ima O. Essiet ◽  
Yanxia Sun
Keyword(s):  
South African ◽  
Demand Response ◽  
Electricity Market ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Cost Effective ◽  
End Users ◽  
Energy Market ◽  
Typical Day ◽  
The Cost

This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.

scholarly journals Smart Energy Management for Unlocking Demand Response in the Residential Sector

Proceedings ◽  
2018 ◽  
Vol 2 (15) ◽  
pp. 1136 ◽  
Author(s):  
Xiangping Chen ◽  
Kui Weng ◽  
Fanlin Meng ◽  
Monjur Mourshed
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Residential Sector ◽  
Cost Of Energy ◽  
Smart Energy Management ◽  
Electrical Demand ◽  
The Uk ◽  
The Cost ◽  
Pv Generation

This paper presents a smart energy management system for unlocking demand response in the UK residential sector. The approach comprises the estimation of one-hour energy demand and PV generation (supply) for scheduling the 24-h ahead demand profiles by shifting potential flexible loads. Real-time electrical demand is met by combining power supplies from PV, grid and batteries while minimizing consumer’s cost of energy. The results show that the peak-to-average ratio is reduced by 22.9% with the cost saving of 34.6% for the selected day.

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 Reducing High Energy Demand Associated with Air-Conditioning Needs in Saudi Arabia

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 87 ◽  
Author(s):  
Jubran Alshahrani ◽  
Peter Boait
Keyword(s):  
Saudi Arabia ◽  
Energy Consumption ◽  
Energy Demand ◽  
Air Conditioning ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
High Energy ◽  
Residential Sector ◽  
Smart Control ◽  
Ac Systems

Electricity consumption in the Kingdom of Saudi Arabia (KSA) has grown at an annual rate of about 7% as a result of population and economic growth. The consumption of the residential sector accounts for over 50% of the total energy generation. Moreover, the energy consumption of air-conditioning (AC) systems has become 70% of residential buildings’ total electricity consumption in the summer months, leading to a high peak electricity demand. This study investigates solutions that will tackle the problem of high energy demand associated with KSA’s air-conditioning needs in residential buildings. To reduce the AC energy consumption in the residential sector, we propose the use of smart control in the thermostat settings. Smart control can be utilized by (i) scheduling and advance control of the operation of AC systems and (ii) remotely setting the thermostats appropriately by the utilities. In this study, we model typical residential buildings and, crucially, occupancy behavior based on behavioral data obtained through a survey. The potential impacts in terms of achievable electricity savings of different AC operation modes for residential houses of Riyadh city are presented. The results from our computer simulations show that the solutions intended to reduce energy consumption effectively, particularly in the advance mode of operation, resulted in a 30% to 40% increase in total annual energy savings.

scholarly journals The Forecasting of Electrical Energy Consumption in Morocco with an Autoregressive Integrated Moving Average Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mohammed Jamii ◽  
Mohamed Maaroufi
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Primary Energy ◽  
Electrical Energy Consumption ◽  
Electricity Grid ◽  
Autoregressive Integrated Moving Average ◽  
Annual Period

The national demand for primary energy has experienced an average increase of almost 5% in recent years, driven by the growth in electricity consumption, which grew by an average of 6% per year between 2003 and 2017, by virtue of the almost generalization of rural electrification and the dynamism of our economy and especially the policy of major works in infrastructure, industry, agriculture, tourism, and social housing. In fact, forecasting the demand for electrical energy remains a controversial issue in the development of the electricity grid and energy management. The ARIMA (1, 1, 1) model is applied to model electrical energy consumption for the annual period from 1971 to 2020. The same data are also used to predicting for 2021–2030 in order to verify the adequacy of the model and to provide information on the state of energy demand in Morocco in the future. The main results indicate an upward trend in electrical energy consumption by the end of 2030, with electricity consumption expected to be in the range of 2039639.09–53589.00 GWh per year.

scholarly journals Impact of Passive Energy Efficiency Measures on Cooling Energy Demand in an Architectural Campus Building in Karachi, Pakistan

2021 ◽  
Vol 13 (13) ◽  
pp. 7251
Author(s):  
Mushk Bughio ◽  
Muhammad Shoaib Khan ◽  
Waqas Ahmed Mahar ◽  
Thorsten Schuetze
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Information Modeling ◽  
Base Case ◽  
Energy Efficiency Measures ◽  
Efficiency Measures ◽  
The Impact ◽  
Cooling Energy Demand ◽  
Campus Building

Electric appliances for cooling and lighting are responsible for most of the increase in electricity consumption in Karachi, Pakistan. This study aims to investigate the impact of passive energy efficiency measures (PEEMs) on the potential reduction of indoor temperature and cooling energy demand of an architectural campus building (ACB) in Karachi, Pakistan. PEEMs focus on the building envelope’s design and construction, which is a key factor of influence on a building’s cooling energy demand. The existing architectural campus building was modeled using the building information modeling (BIM) software Autodesk Revit. Data related to the electricity consumption for cooling, building masses, occupancy conditions, utility bills, energy use intensity, as well as space types, were collected and analyzed to develop a virtual ACB model. The utility bill data were used to calibrate the DesignBuilder and EnergyPlus base case models of the existing ACB. The cooling energy demand was compared with different alternative building envelope compositions applied as PEEMs in the renovation of the existing exemplary ACB. Finally, cooling energy demand reduction potentials and the related potential electricity demand savings were determined. The quantification of the cooling energy demand facilitates the definition of the building’s electricity consumption benchmarks for cooling with specific technologies.

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