The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for future research in this area. We apply a number of forecasting methods including ARIMA, neural networks, and exponential smoothening using several strategies for training data selection, in particular day type and sliding window based strategies. We consider forecasting horizons ranging between 15 minutes and 24 hours. Our evaluation is based on two data sets containing the power usage of individual appliances at second time granularity collected over the course of several months. The results indicate that forecasting accuracy varies significantly depending on the choice of forecasting methods strategy and the parameter configuration. Measured by the Mean Absolute Percentage Error (MAPE), the considered state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Overall, we observed MAPEs in the range between 5 and >100 . The average MAPE for the first data set was 30 , while it was 85 for the other data set. These results show big room for improvement. Based on the identified trends and experiences from our experiments, we contribute a detailed discussion of promising future research.
Demand side management and demand response receive increasing attention by research and industry. The research work published so far includes a variety of directions from direct load control or targeted customer interaction to indirect incentive-based control (see @cite_18 for an overview). In order to help balancing demand and supply, demand side management programs require accurate predictions of consumer demand.
@cite_18
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This paper mainly focuses on demand side management and demand response, including drivers and benefits, shiftable load scheduling methods and peak shaving techniques. Demand side management techniques found in literature are overviewed and a novel electricity demand control technique using real-time pricing is proposed. Currently users have no means to change their power consumption to benefit the whole system. The proposed method consists of modern system identification and control that would enable user side load control. This would potentially balance demand side with supply side more effectively and would also reduce peak demand and make the whole system more efficient. Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and load-shifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings. © 2014 IEEE. Demand-side Demand-side control is playing an increasingly important role in smart grid control strategies. Modeling the dynamical behavior of a large population of appliances is especially important to evaluate the effectiveness of various load control strategies. In this paper, a high accuracy aggregated model is first developed for a population of HVAC units. The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. Furthermore, the model takes into account the lockout effect of the compressor in order to represent the dynamics of the system under control more accurately. Then, a novel closed loop load control strategy is designed to track a desired demand curve and to ensure a stable and smooth response. Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges. Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme. Demand response on the residential market is becoming a solution to adapt customer consumption to the offer available and therefore lower the electricity peak prices. Tariff incentives and direct load control of residential air-conditioners and electric heaters are flexible solutions to reduce the peak demand. To include residential demand response resources in planning operators, quantifying the demand reduction is becoming a major issue for all electrical stakeholders. Current methods are based on day or weather matching, regressions and control group approaches. In general, methods using available data from a control group give more accurate results. With the introduction of smart meters, the electric utilities generate a large amount of quality data, available almost in real time. In this paper, we suggest using these available residential load curves to select a control group based on individual load curves. One of the advantages of our method is that the selected control group could adapt at anytime to the number of individuals belonging to the demand reduction program, as this number evolves with customers entering and leaving the program. Constrained regression methods and an algorithm are developed and evaluated on real data, providing a reliable solution for an operational use. Demand response is a key component of existing and future grid systems facing increased variability and peak demands. Scaling demand response requires efficiently predicting individual responses for large numbers of consumers while selecting the right ones to signal. This paper proposes a new online learning problem that captures consumer diversity, messaging fatigue and response prediction. We use the framework of multi-armed bandits model to address this problem. This yields simple and easy to implement index based learning algorithms with provable performance guarantees. Recently issued U.S. Federal Energy Regulatory Commission regulations require comparable treatment of demand reduction and generation in the wholesale electric market so that they are compensated at the same market clearing price. The new regulations measure demand reduction as a reduction from a “customer baseline,” a historically based estimate of the expected consumption. In this paper, we study the incentive effects on the efficiency of the demand response regulation using a static equilibrium model and a dynamic extension of the model. Our analysis provides three main results. Firstly, our analysis shows that the demand reduction payment will induce consumers to (1) inflate the customer baseline by increasing consumption above the already excessive level during normal peak periods and (2) exaggerate demand reduction by decreasing consumption beyond the efficient level during a demand response event. This result persists when applied to alternative baseline designs in a dynamic model. Secondly, we study alternative policy remedies to restore the efficiency of demand response regulation and introduce a new approach to define the customer baseline as a fixed proportion of an aggregate baseline. In particular, the aggregate baseline approach can significantly weaken or eliminate the incentive to inflate the baseline. Finally, we illustrate that if the baseline inflation problem is solved and demand and supply functions are linear, the current policy can produce a net social welfare gain. However, the welfare improvement requires that demand reduction be paid only when the wholesale price is at least twice the fixed retail rate. This argues that the policy should include a sufficiently high threshold price below which demand response is not dispatched. Demand response aims at utilizing flexible loads to operate power systems in an economically efficient way. A fundamental question in demand response is how to conduct a baseline estimation to deal with increasing uncertainties in power systems. Unfortunately, traditional baseline estimation lacks the ability to characterize uncertainties due to their deterministic modeling. This deficiency often results in erroneous system operations and miscalculated payments that discourage participating customers. In this paper, we propose a Gaussian process-based approach to mitigate the problem. It features the ability to use all historical data as a prior knowledge, and adjust the estimation according to similar daily patterns in the past. To characterize the uncertainties, this method provides a probabilistic estimate that can be used to not only increase estimation confidence for system operators but also to fairer treatment to customers. Finally, simulation results from Pacific Gas and Electric Company data show that this new method can produce a highly accurate estimate, which dramatically reduces the uncertainties inherent in the distribution power grid. Such a work opens the door for power system operation based on probabilistic estimate. In this paper, we consider the demand response problem in smart grid consisting of a retailer and multiple residential consumers, where the retailer determines consumers' payments based on their power consumption profile. Our aim is to propose a fully distributed algorithm that is able to optimize the aggregate cost, utility, and retailer's profit simultaneously. To this end, we first formulate the consumer-side trend as a constrained convex optimization problem and propose a fully distributed adaptive diffusion algorithm to solve it. In addition, we design a one-leader N-follower Stackelberg game to model interactions among the retailer and consumers. The proposed framework is able to continuously track the drifts resulting from the changes in the real-time pricing or the consumer preferences. Moreover, it is scalable and does not require network-wide information or rely on central controller. We provide comprehensive simulation results to show the effectiveness of the proposed framework. The stringent requirement of the demand-supply equilibrium for delivering electricity has traditionally been dealt with a supply-side perspective, assuming that the demand is not alterable. With the promises of the smart grid, demand-side management techniques are increasingly becoming more feasible. A demand-side management technique, called demand response (DR), aims at inducing changes in electricity load in response to financial incentives, some of which involve bidding as the underlying facilitator. It is well-established that the effectiveness of the DR is proportional to the number of participants. Yet, many of the DR programs, including those involving bidding, may suffer due to consumer privacy concerns. Within this context, in this paper, we propose a distributed and multi-unit privacy guaranteeing bidding mechanism as part of a DR program without relying on any third party, trusted or not, to protect the participants’ bidding information, except obviously for the winning price and the winner exposed to the utility. To the best of our knowledge, this is the first such approach for the DR bidding programs. We provide a security analysis of our approach under the honest-but-curious and active adversary assumptions and prove the privacy assuring property. Real-time price setting strategies are investigated for use by demand response programs in future power grids. The major challenge is that consumers have varying degrees of responsiveness to price adjustments at different time instants, which must be learned and accounted for by demand response initiatives. To this end, an online learning approach is developed here offering strong performance guarantees with minimal assumptions on the dynamics of load levels and consumer elasticity, even when consumers are adversarial and take actions strategically. The developed algorithms can determine electricity prices sequentially so as to elicit desirable usage behavior and flatten load curves, while implicitly learning individual consumers’ price elasticity based on available feedback information. Two feedback structures are considered: 1) a full information setup, where aggregate load levels as well as individual price elasticity parameters are directly available, and 2) a partial information (bandit) case, where only the aggregate load levels are revealed. Fairness and sparsity constraints are also incorporated via appropriate regularizers. Numerical tests verify the effectiveness of the proposed approach. Marketing researchers have used models of consumer demand to forecast future sales, to describe and test theories of behavior, and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain an econometric system that describes consumers' choices over available options, and to thus characterize product demand. The basic framework has been augmented significantly to account for quantity choices, to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multicategory purchases), and to allow for asymmetric switching between brands across different price tiers. These extensions have enabled researchers to bring the analysis to bear on several related marketing phenomena of interest. This paper has three main objectives. The first objective is to articulate the main goals of demand analysis---forecasting, measurement, and testing---and to highlight several considerations associated with these goals. Our second objective is to describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and we review extensions that have appeared in the marketing literature. The third objective is to explore a few emerging directions in demand analysis, including considering demand-side dynamics, combining purchase data with primary information, and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see the potential for new directions in future research. Demand-side management in smart grids has emerged as a hot topic for optimizing energy consumption. In conventional research works, energy consumption is optimized from the perspective of either the users or the power company. In this paper, we investigate how energy consumption may be optimized by taking into consideration the interaction between both parties. We propose a new energy price model as a function of total energy consumption. Also, we propose a new objective function, which optimizes the difference between the value and cost of energy. The power supplier pulls consumers in a round-robin fashion and provides them with energy price parameter and current consumption summary vector. Each user then optimizes his own schedule and reports it to the supplier, which, in turn, updates its energy price parameter before pulling the next consumers. This interaction between the power company and its consumers is modeled through a two-step centralized game, based on which we propose our game-theoretic energy schedule (GTES) method. The objective of our GTES method is to reduce the peak-to-average power ratio by optimizing the users' energy schedules. The performance of the GTES approach is evaluated through computer-based simulations. Electricity demand response refers to consumer actions that change the utility load profile in a way that reduces costs or improves grid security. The focus of demand response has mainly been on the commercial and big industrial sectors because of the large demand reduction that they can offer to the utility grid operators. Utilities are showing increasing interest in residential demand response (RDR). RDR can be treated as an energy resource which can be assessed and commercially developed, however, there are still some issues that remain to be addressed for RDR to be successful. These include price unresponsiveness of some residential consumers, equity issues and high cost of the metering infrastructure. The aim of this paper is to investigate and present some of the challenges in achieving effective voluntary demand reduction based on a review of residential demand response literature as well as the general residential energy use behaviour literature. The authors propose the use of a hybrid engineering approach using social psychology and economic behaviour models to overcome these challenges and realize the benefits of supply security and cost management. Response of demand, distributed generation and electricity storage (e.g. vehicle to grid) will be crucial for power systems management in the future smart electricity grid. Currently, demand response approaches are predominantly based on one-way signalling from the utility to the end-customer's devices and installations. However, this approach leads to a suboptimal solution as it does not unlock the full flexibility potential of these devices. On top of that, it leads to market inefficiencies and uncertainty in the price response of end-customer's demand and supply units when applied on a mass scale. These drawbacks are not present when the responsive end-customer's systems are fully integrated in the electricity markets. In this paper, we describe a smart grid technology that integrates demand and supply flexibility in the operation of the electricity system, rather than just letting it respond. Over the last few years, this technology has been researched and developed into a market-ready system, and has been deployed in a number of successful field trials. Using this approach, the pool reaction to a certain price signal is known beforehand. With that, demand response moves from influencing, with an uncertain overall response, into control with the dynamic price as a control signal. In this paper, we study Demand Response (DR) problematics for different levels of information sharing in a smart grid. We propose a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and study how close they can get to an ideal flat profile depending on how much information they share. When customers can share all their load profiles, we provide a distributed algorithm, set up as a cooperative game between consumers, which significantly reduces the total cost and peak-to-average ratio (PAR) of the system. In the absence of full information sharing (for reasons of privacy), when users have only access to the instantaneous total load on the grid, we provide distributed stochastic strategies that successfully exploit this information to improve the overall load profile. Simulation results confirm that these solutions efficiently benefit from information sharing within the grid and reduce both the total cost and PAR. Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning ‘bottom-up’ activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are under-represented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response. The price set in electricity markets is given by the intersection of supply and demand during a given time period. The demand-side has traditionally been a price taker while the supply-side actively adjusts the output of the market clearing unit to fluctuations in consumption. Currently, there is a transition toward active demand participation that can adequately respond to market conditions. However, private knowledge of demand adjustments, such as the impact of modifying behavior or the availability to do so, creates asymmetry of information between the active supply side and the passive demand. This paper proposes a revelation mechanism that will prompt the demand-side to choose the best option for themselves among a menu of incentives. Rational behavior of consumers implies that demand will only shift when the benefit of doing so is higher than the costs of modifying consumption patterns. Given differences among demand participants and the objectives of the market operator, an analysis of the rationale of each market agent shows the feasible options for demand incentives. This study enables the design of appropriate market mechanisms aimed to discover customer categories and determine the adequate incentives for each case. Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers’ subjective behavior on each other’s DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies, which assume that customers are rational in their decision-making, a novel approach is proposed based on the framework of prospect theory (PT) to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious play is proposed using which the game will reach an @math -mixed Nash equilibrium. Simulation results are provided to assess the impact of customer behavior on DSM. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendencies.