Novel genetic algorithm for scheduling of appliances

YesThe introduction of smart metering has brought more detailed information on the actual load profile of a house. With the ability to measure, comes the desire to control the load profile. Furthermore, advances in renewable energy have made the consumer to become supplier, known as Prosumer, who therefore also becomes interested in the detail of his load, and also his energy production. With the lowering cost of smart plugs and other automation units, it has become possible to schedule electrical appliances. This makes it possible to adjust the load profiles of houses. However, without a market in the demand side, the use of load profile modification techniques are unlikely to be adapted by consumers on the long term. In this research, we will be presenting work on scheduling of energy appliances to modify the load profiles within a market environment. The paper will review the literature on algorithms used in scheduling of appliances in residential areas. Whilst many algorithms presented in the literature show that scheduling of appliances is feasible, many issues arise with respect to user interaction, and hence adaptation. Furthermore, the criteria used to evaluate the algorithms is often related only to reducing energy consumption, and hence CO2. Whilst this a key factor, it may not necessarily meet the demands of the consumer. In this paper we will be presenting work on a novel genetic algorithm that will optimize the load profile while taking into account user participation indices. A novel measure of the comfort of the customer, derived from the standard deviation of the load profile, is proposed in order to encourage the customer to participate more actively in demand response programs. Different scenarios will also be tested.This work was supported by the British Council and the UK Department of Business Innovation and Skills under GII funding for the SITARA project

Investigating the impact of discomfort in load scheduling using genetic algorithm

YesEnergy consumers oftentimes suffer some element of discomfort associated with the implementation of demand response programs as they aim to follow a suggested energy consumption profile generated from scheduling algorithms for the purpose of optimizing grid performance. This is because people naturally do not like to be told what to do or when to use their appliances. Although advances in renewable energy have made the consumer to also become energy supplier, who can actively cash in at times of the day when energy cost is high to either sell excess energy generated or consume it internally if required, thereby nullifying the adverse effect of this discomfort. But a majority of consumers still rely wholly on the supply from the grid. This impact on users' comfort who are active participants in demand response programs was investigated and ways to minimizing load scheduling discomfort was sought in order to encourage user participation

Short term energy forecasting techniques for virtual power plants

YesThe advent of smart meter technology has enabled periodic monitoring of consumer energy consumption. Hence, short term energy forecasting is gaining more importance than conventional load forecasting. An Accurate forecasting of energy consumption is indispensable for the proper functioning of a virtual power plant (VPP). This paper focuses on short term energy forecasting in a VPP. The factors that influence energy forecasting in a VPP are identified and an artificial neural network based energy forecasting model is built. The model is tested on Sydney/ New South Wales (NSW) electricity grid. It considers the historical weather data and holidays in Sydney/ NSW and forecasts the energy consumption pattern with sufficient accuracy

Evaluation of community virtual power plant under various pricing schemes

YesTechnological advancement on the electricity grid has focused on maximizing its use. This has led to the introduction of energy storage. Energy storage could be used to provide both peak and off-peak services to the grid. Recent work on the use of small units of energy storage like battery has proposed the vehicle to grid system. It is propose in this work to have energy storage device embedded inside the house of the energy consumer. In such a system, consumers with battery energy storage can be aggregated in to a community virtual power plant. In this paper, an optimized energy resource allocation algorithm is presented for a virtual power plant using genetic algorithm. The results show that it is critical to have a pricing scheme that help achieve goals for grid, virtual power plant, and consumers.Mr. Oghenovo Okpako is grateful to the Niger Delta Development Commission of Nigeria for funding the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project

Investigation of an optimized energy resource allocation algorithm for a community based virtual power plant

YesRecently, significant advances in renewable energy generation have made it possible to consider consumers as prosumers. However, with increase in embedded generation, storage of electrical energy in batteries, flywheels and supercapacitors has become important so as to better utilize the existing grid by helping smooth the peaks and troughs of renewable electricity generation, and also of demand. This has led to the possibility of controlling the times when stored energy from these storage units is fed back to the grid. In this paper we look at how energy resource sharing is achieved if these storage units are part of a virtual power plant. In a virtual power plant, these storage units become energy resources that need to be optimally scheduled over time so as to benefit both prosumer and the grid supplier. In this paper, a smart energy resources allocation algorithm is presented for a virtual power plants using genetic algorithms. It is also proposed that the cause of battery depreciation be accounted for in the allocation of discharge rates. The algorithm was tested under various pricing scenarios, depreciation cost, as well as constraint. The results are presented and discussed. Conclusions were drawn, and suggestion for further work was made.Mr. Oghenovo Okpako is grateful for the support of the Niger Delta Development Commission of Nigeria for supporting the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

The influence of different tariffs schemes on electricity consumption for the UK domestic buildings.

yesElectricity Suppliers in competitive electricity markets commonly respond to prices changes which are fluctuating over time, but most consumers respond to the price changes as reflected on their electricity bills. Almost all consumers pay fixed tariffs for their consumption without distinctions based on usage time, so these consumers have had no incentives to reduce their use during the peak times. This paper aims to analyze the influence of different tariff schemes on consumer behaviours in UK domestic buildings. A realistic half hourly electricity load profile for different types of UK households that based mainly on public reports and statistics has been generated. This load profile data were used to help calculate the expected change in consumers' bills under standard tariffs offered from different suppliers to what the cost of electricity would be under time varying tariff (economy7 tariff) and to estimate of how much consumers would shift their load in response to price changes without changing total consumption, for which the results are presented and discussedMSCR

Statistical Predictions of Electric Load Profiles in the UK Domestic Buildings.

yesThis paper presents a method of generating realistic electricity load profile data for the UK domestic buildings. The domestic space heating and domestic hot water have been excluded in this study. The information and results of previous investigations and works that is available in public reports and statistics have been used as input data when modeling of domestic energy consumption. A questionnaire survey was conducted to find out what occupants do in different times of the day in order to get probabilistic estimates of usage of electrical household. The daily energy demand load profile of each appliance can be predicted using this method. A measured data set is also applied for comparison, and verification. Our analysis shows that the generated load profiles have a good agreement with real data. The daily load profile from individual dwelling to community can be predicted using this method

Determination of static voltage stability-margin of the power system prior to voltage collapse.

yesVoltage instability problems in power system are an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know how far the power systems from voltage collapse in order to apply suitable action to avoid unexpected results. This paper propose a review of some static voltage stability indices found in the literature to study voltage collapse reveals that various analytical tools based on different concept to predict voltage collapse phenomena. These static voltage stability indices present reliable information about the closeness of the power system to voltage collapse and identification of the weakest bus, line and area in the power network. A number of static voltage stability indices have been proposed in the literature, but in this only four of them will be considered. The effectiveness of these indices is demonstrated through studies in IEEE 14 bus reliability test system. The results are discussed and key conclusion presented.MSCR

Weakest Bus Identification Based on Modal Analysis and Singular Value Decomposition Techniques.

yesVoltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3-machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed