Electricity Demand Of Consumers Research Articles

THE PARTICIPATION OF RESIDENTIAL CONSUMERS IN ENERGY MARKETS THROUGH DEMAND RESPONSE

Abstract. The article considers the approach to the interaction of the transmission system operator with consumers - the demand management mechanism, as well as its role and place in the energy markets. The tools and experience of implementation of demand management are described. With the help of a natural experiment, the possibility and effectiveness of the participation of consumers with household storage hot water boilers in the balancing market was evaluated. Optimizing the participation of large numbers of consumers in energy markets is a challenging problem, especially for portfolios with thousands or millions of flexible resources. One of the aggregator's tasks is to transform the functionality of automation technologies, such as a consumer's "smart" home, into products that could be traded on energy markets. The task of optimization consists in the effective and timely management of a large number of flexible resources. A key feature of demand management is the sale of managed load on a par with generating capacity. This is explained by the fact that, from the point of view of the balance, in the energy system, one kWh not consumed equals one kWh produced. As a result, the financial benefits of managing consumer electricity demand are equivalent to the prices of balancing services and the provision of capacity reserves from other market participants. The sale of a demand-side management resource can be implemented through balancing market (BM) and ancillary service market (SMS) instruments, which use TSOs to balance the power system. Attracting additional participants in RDP and BR should stimulate competition and allow TSOs to meet their needs for auxiliary and balancing services, which will positively affect the reliability of the energy system and increase competition. In general, the level of provision of the power system with reserves depends on a wide range of factors, including: different hours of the day, weather conditions or trading strategies of market participants, etc. The optimal way to involve household consumers in responding to demand is to use the services of aggregators. But this does not mean the simple accumulation of a certain resulting power of consumers. The quantitative and qualitative composition of the aggregator's portfolio should ensure the execution of OSP commands in full, even in conditions of unavailability of part of the boilers or other aggregated units. For this, it is necessary to have a certain reserve of regulating power, for example, other boilers or energy storage units for the possibility of their prompt replacement (reconfiguration) within the portfolio. Demand management in the power system can effectively reduce the peak load of the system and delay the necessary capital investment in additional generating capacity and transmission lines. In addition, demand management facilitates the integration of renewable energy sources and reduces the costs of setting up, starting or shutting down thermal power units during peak load periods. Consumer participation in demand management requires bidirectional communication infrastructure, advanced metering, efficient economic tariffs and energy management algorithms. With the rise of smart grid technologies and automated demand response systems, demand management systems are a key element expected to provide a cost-effective alternative to traditional generation-side solutions to meet increasing demand for electricity during peak load or high prices. Demand management competes with auxiliary services provided by "traditional" providers - power plants and is used by TSOs to stabilize UES parameters when the power grid faces unforeseen situations, accordingly, the development of this sector is an important component of energy security.

Leveraging Deep Q-Learning to maximize consumer quality of experience in smart grid

The smart grid system has addressed the problems of traditional power grid by not only meeting energy demand in real-time but also limiting its wastage. The two key objectives of a smart grid system are to ensure a higher Quality of Experience (QoE) for consumers and to reduce consumer costs using dynamically varying pricing concepts. However, these two objective parameters oppose each other as maximizing the consumer QoE requires the availability of sufficient electric power at any given time, which in turn increases power purchase cost. In this paper, we introduce an efficient power management system architecture of a smart grid and develop an Optimal Energy Allocation and Prediction system based on Deep Q-Leaning, namely OEAP-DQL, that brings a trade-off between the two. The developed OEAP-DQL system is a multi-objective linear programming (MOLP) problem that predicts consumer electricity demand by exploiting four different weighted and regressive moving average forecasting methods in the action space to accurately capture dynamically varying customer demand behaviors. Furthermore, iterative exploitation of multiple learning methods decreases forecasting error and intelligent stored power management helps the OEAP-DQL smart grid operator (SGO) to enhance its profit. The results of our simulation experiments show that the OEAP-DQL system outperforms the state-of-the-art works in terms of QoE and cost.

Optimal real-time pricing and electricity package by retail electric providers based on social learning

As the global retail electricity market liberalizes, retail electric providers (REPs) are optimizing pricing schemes and electricity packages (EPs) to meet consumers' individual and diverse electricity plans while reducing volatility risk and ensuring profitability. However, consumer social learning (SL) helps frequently adjust electricity plans as social media evolves, preventing REPs from accurately understanding consumer electricity demand. Therefore, this paper constructs a dynamic game model of the retail electricity market considering social learning. REPs announce price and electricity package information to consumers based on market information and historical experience, and consumers first shared electricity usage plans based on their willingness to learn and share information among themselves, and then respond to REPs based on current information and experience. The main findings of this paper are as follows: (i) social learning increases consumer utility, promotes competition, depresses REPs prices, shrinks price differentials, and induces concessions by REPs, but raises volatility risk. (ii) electricity packages combined with social learning can save electricity consumption and significantly reduce volatility risk. Finally, this paper uses Smart Meter Energy Consumption data in London households to demonstrate the impact of social learning and to provide decision support for REP's electricity sales strategies.

Robust Mixed-Integer Programing Model for Reconfiguration of Distribution Feeders Under Uncertain and Variable Loads Considering Capacitor Banks, Voltage Regulators, and Protective Relays

Feeder reconfiguration is an effective way to reduce power losses of distribution network. In this way, configuration of distribution system is changed in order to achieve possible minimum losses, while electricity demand of consumers has to be provided. Consumers’ power demand has an important role in feeder reconfiguration because any change in demand affects power losses directly. Whereas load demand has a variable and stochastic nature because of its dependence on consumption pattern and accuracy of forecasted load amounts. Accordingly, reconfiguration models should be robust enough against load uncertainty and variations. Thus, this article presents an efficient robust model for reconfiguration of distribution feeders under uncertain and variable loads. The proposed reconfiguration model is robust enough and efficient, in which its implementation is relatively simple. The results show higher efficiency and lower complexity of the proposed model compared to existing robust reconfiguration approaches.

Negawatt Planning for Aggregation Coordinator Considering Fluctuations in Electricity Demand of Consumers

Power suppliers generate electricity to ensure that the power generated equals demand. The vast daily fluctuations in electricity demand result in very high generation costs. Demand response is attracting attention as a solution to these problems in recent years with the spread of renewable energy, energy resources such as storage batteries, and the development of IoT technologies. Demand response means changing the power demand pattern by controlling demand side energy resources and distributed energy resources. In recent years, the use of demand response and the construction of mechanisms for it are being promoted. In this study, we focus on negawatt trading that is one of the demand response policies. We formulate a stochastic programming model for negawatt planning operation and the efficient solution method are shown. From experiment results, it is shown that the worst situation can be avoided using CVaR model. We also showed the efficiency of solution.

Determinants of Demand Response Program Participation: Contingent Valuation Evidence from a Smart Thermostat Program

As renewable electricity generation continues to increase in the United States (US), considerable effort goes into matching heterogeneous supply to demand at a subhour time-step. As a result, some electric providers offer incentive-based programs for residential consumers that aim to reduce electric demand during high-demand periods. There is little research into determinants of consumer response to incentive-based programs beyond typical sociodemographic characteristics. To add to this body of literature, this paper presents the findings of a dichotomous choice contingent valuation (CV) survey targeting US ratepayers’ participation in a direct-load-control scheme utilizing a smart thermostat designed to reallocate consumer electricity demand on summer days when grid stress is high. Our results show approximately 50% of respondents are willing to participate at a median willingness-to-accept (WTA) figure of USD 9.50 (95% CI: 3.74, 15.25) per month that lasts for one summer (June through August)—or slightly less than USD 30 per annum. Participation is significantly affected by a respondent’s attitudes and preferences surrounding various environmental and institutional perspectives, but not by sociodemographic characteristics. These findings suggest utilities designing direct-load-control programs may improve participation by designing incentives specific to customers’ attitudes and preferences.

Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.

Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy

This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms; thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models.

Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

An agent-based Stackelberg framework for joint expansion planning of privately Owned regional energy systems and sub-transmission grid

Traditionally, a regional electric company (REC) should supply electricity demand of consumers at sub-transmission level in special geographic areas. As a promising alternative for traditional centralized planning, in this paper, a Stackelberg game approach is employed to solve the investment and operational expansion planning problems in the presence of privately owned Regional Energy Systems (RGESs) and multi-agent systems are used to ease the participation of private ownerships in planning model by preventing agents to disclose their private properties. The RGES's and the REC's agents are the active players of the proposed method and in the form of a semi-decentralized framework, the information exchange among them is fully decentralized. Also, an Independent Coordinating Agent (ICA) is centrally responsible for Stackelberg Nash equilibrium point extraction. Further, a bi-stage Stackelberg game approach is proposed for coupled expansion planning program of Sub-transmission network and privately owned RGESs. To validate the feasibility and efficacy of the suggested model, a modified electric sub-transmission system (located in Guilan Province, Islamic Republic of Iran) is employed as a realistic test case system for simulations. And, the obtained results of simulations by the proposed method are compared with conventional expansion planning of sub-transmission systems.

Feasibility study on Coordinated Control of Heating Ventilation and Air Conditioning Loads and Battery Energy Storage System for Improved Performance of FastADR Response

Abstract Fast Automatic Demand Response (FastADR) system is one of the expected measures to control electricity demand of consumers so that the electricity supply – demand balancing in a short time horizon is improved. Due to their high power consumption in business buildings, Heating Ventilation and Air-conditioning (HVAC) loads are one of the promising candidates on demand-side for FastADR. However, HVAC loads do not perfectly follow external control signals due to the operational characteristics. Although Battery Energy Storage System (BESS) can be useful for supplementing power consumption of an HVAC load in following FastADR signal, its State of Charge (SOC) should be recovered quickly in order to prepare for the next FastADR signal or other purposes, and to reduce the energy capacity of BESS. This paper proposes and numerically demonstrates a coordinated control of HVAC loads and BESS to restore BESS’s SOC without affecting total electricity load and indoor temperature.

Solar PV where the sun doesn’t shine: Estimating the economic impacts of support schemes for residential PV with detailed net demand profiling

Countries with low irradiation and solar PV adoption rates are increasingly considering policy support for solar PV, although consumer electricity demand and solar generation profiles are often mismatched. This paper presents a methodology for policymakers in such conditions to examine more precisely the financial performance of residential solar PV from the consumer perspective as part of an ex-ante policy assessment. We model a range of prospective policy scenarios and compare policy mechanisms that compensate homeowners for generation, those that reduce their upfront costs, and those that assist with financing, using Ireland as a case study. The results confirm the intuitive notion that more generous financial remuneration schemes provide quicker payback; however we observe that in low irradiance regions there is little difference between upfront grants and feed-in-tariffs to accelerate payback timeframes. We also show the importance of retail tariff structure in consumer payback for solar PV systems, with one-part tariffs generating shorter paybacks than two-part tariff structures, although the latter is more likely to secure revenue for electricity infrastructure investment. Drawing from this analysis, the paper proposes some options for the design of policy supports and tariff structures to deliver a sustainable residential renewable electricity system in low-irradiance regions.

Prediction of available power being generate in small hydropower system at Sungai Perting Bentong Pahang

Tremendous studied had been conducted on small hydropower system based on run-of-river schemes as an alternative renewable energy. Small hydropower system can be classified based on electricity generated between 1MW to 10MW. This system is normally being applied in rural area for providing the consumer electricity demand. Basically the researches to date are more focusing on the large scale of hydropower rather than the small scale hydropower technology. Therefore, this study is aimed to focus on predicting the available power generated by the small hydropower system specifically for the river stream in peninsular Malaysia. The water flow rate is measured by using ultrasonic level sensor located at the intake of the small hydropower system. The water flow rate is important data to be used in predicting the power output of the power house. The result shows that, the power outputs are depending on the fluctuation of water flow rate and the electricity being generated is more than 1MW. This finding can be used as the benchmark for daily and monthly monitoring process of the system efficiency or target output.

Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid

Distributed renewable electricity generators facilitate decarbonising the electricity network, and the smart grid allows higher renewable penetration while improving efficiency. Smart grid scenarios often emphasise localised control, balancing small renewable generation with consumer electricity demand. This research investigates the applicability of proposed decentralised smart grid scenarios utilising a mixed strategy: quantitative analysis of PV adoption data and qualitative policy analysis focusing on policy design, apparent drivers for adoption of the deviation of observed data from the feed-in tariff impact assessment predictions. Analysis reveals that areas of similar installed PV capacity are clustered, indicating a strong dependence on local conditions for PV adoption. Analysing time series of PV adoption finds that it fits neither neo-classical predictions, nor diffusion of innovation S-curves of adoption cleanly. This suggests the influence of external factors on the decision making process. It is shown that clusters of low installed PV capacity coincide with areas of high population density and vice versa, implying that while visions of locally-balanced smart grids may be viable in certain rural and suburban areas, applicability to urban centres may be limited. Taken in combination, the data analysis, policy impact and socio-psychological drivers of adoption demonstrate the need for a multi-disciplinary approach to understanding and modelling the adoption of technology necessary to enable the future smart grid.