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
2062912550
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. Abstract The smart grid, as an emerging cyber-physical system, is attractive because of features such as distributed energy control and robust load fluctuation management. The demand Response (DR) component in smart grids helps in maintaining demand-supply balance and in controlling consumer side electricity bills. One of the visions of smart grids is to have communication between consumers and suppliers facilitate certain types of DR strategies, e.g., demand bidding (DR-DB). DR-DB is an incentive-based DR, where certain incentives are awarded to consumers who participate in DR events. However, privacy and security in the DR-DB bidding process are of paramount importance as potentially sensitive consumer data is used during the process. In this paper, we propose a private and secure bidding protocol for incentive-based demand-response systems using cryptographic primitives without assuming any trusted third-party. We analyze the security and privacy guarantees; and show that the various stages in our proposed demand bidding are computationally feasible, in terms of cryptographic primitives, through performance evaluation on three different computing environments. 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. 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. Dozens of markets of all types are in the early stages of a revolution as the Internet and related technologies vastly expand the variety of products that can be produced, promoted and purchased. Although this revolution is based on a simple set of economic and technological drivers, the authors argue that its implications are far-reaching for managers, consumers and the economy as a whole. This article looks at what has been dubbed the "Long Tail" phenomenon, examining how customers derive value from an important characteristic of Internet markets: the ability of online merchants to help consumers locate, evaluate and purchase a far wider range of products than they can typically buy via the traditional brick-and-mortar channels. The article examines the Long Tail from both the supply side and the demand side and identifies several key drivers. On the supply side, the authors point out how e-tailers' expanded, centralized warehousing allows for more offerings, thus making it possible for them to cater to more varied tastes. On the demand side, tools such as search engines, recommender software and sampling tools are allowing customers to find products outside of their geographic area. The authors also look toward the future to discuss second order amplified effects of Long Tail, including the growth of markets serving smaller niches. 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. 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. 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. We design an optimal contract between a demand response aggregator (DRA) and power grid customers for incentive-based demand response. We consider a setting in which the customers are asked to reduce their electricity consumption by the DRA and they are compensated for this demand curtailment. However, given that the DRA must supply every customer with as much power as she desires, a strategic customer can temporarily increase her base load in order to report a larger reduction as part of the demand response event. The DRA wishes to incentivize the customers both to make costly effort to reduce load and to not falsify the reported load reduction. We model this problem as a contract design problem and present a solution. The proposed contract consists of two parts: a part that depends on (the possibly inflated) load reduction as measured by the DRA and another that provides a share of the profit that accrues to the DRA through the demand response event to the customers. Since this profit accrues due to the total load reduction because of the actions taken by all the customers, the interaction among the customers also needs to be carefully included in the contract design. The contract design and its properties are presented and illustrated through examples. 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. Electric energy storage devices are prime candidates for demand load management in the smart power grid. In this work, we address the optimal energy storage control problem from the side of the utility operator. The operator controller receives power demand requests with different power requirements and durations that are activated immediately. The controller has access to one energy storage device of finite capacity. The objective is to devise an energy storage control policy that minimizes long-term average grid operational cost. The cost is a convex function of instantaneous power demand that is satisfied from the grid, and it reflects the fact that each additional unit of power needed to serve demands is more expensive as the demand load increases. For the online dynamic control problem, we derive a threshold-based control policy that attempts to maintain balanced power consumption from the grid at all times, in the presence of continual generation and completion of demands. The policy adaptively performs charging or discharging of the storage device. The former increases power consumption from the grid and the latter satisfies part of the grid demand from the stored energy. We prove that the policy is asymptotically optimal as the storage capacity becomes large, and we numerically show that it performs very well even for finite capacity. The off-line problem over a finite time horizon that assumes a priori known power consumption to be satisfied at all times, is formulated and solved with Dynamic Programming. Finally, we show that the model, approach and structure of the optimal policy can be extended to also account for a renewable source that feeds the storage device. 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. 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. Reducing greenhouse gas emissions from energy consumption in the UK is increasingly linked to the introduction of uncontrollable sources of power, such as solar PV and wind, and the electrification of energy services (Darby, 2012; Department of Energy and Climate Change, 2013, 2011). Electrifying heating and personal mobility requires moving them off the gas grid and petrol pump and onto the electricity system, which in turn may have implications for the profound peak in electricity use that takes place in the early evening. While the conventional response would be to reinforce electricity networks, under conditions where there are an increasing range and diversity of sources of electricity generation that are less predictable and controllable, there is increased political and commercial interest in managing demand in these periods. Demand Side Management (DSM) enabled by smart grids promises to bring consumers of electricity into the management of the grid by asking them to provide the flexibility and responsiveness that the industry may lose in the future. In this paper we draw on 186 qualitative home tours in the UK to examine how such forms of flexibility are constituted. Rather than seeing flexibility as related to the characteristics of individuals and their behaviour, as is common in the industry and policy, we argue that it is the social practices which shape electricity demand curves that need to be at the centre of analysis. To illustrate this argument, we consider how, in what ways and for what purposes consumption of electricity may be or become flexible in response to a time of use tariff designed to reduce consumption in the early evening. We argue that the rhythmic qualities of practices and the degree to which there are socially conventional times and ways to perform particular practices can constrain or open up their ability to adapt to interventions. Due to the stochastic nature of wind power, its large-scale integration into the power grid requires techniques to constantly balance the load with the time-varying supply. This can be done via smart scheduling of energy consumption and storage units among end users. In this paper, we propose a game-theoretic algorithm to be implemented in an aggregator in order to coordinate the operation of demand-side resources via pricing in order to tackle the intermittency and fluctuations in wind power generation. The demand-side resources to be considered are both non-shiftable and shiftable load, in particular, electric vehicles that charge or discharge their batteries to provide extra resource management flexibility. After formulating the interactions in an aggregator-consumer system as a game, we analytically prove the existence and uniqueness of the Nash equilibrium in the formulated game model. Simulation results show that our proposed design scheme can benefit both end users (in terms of reducing energy expenses) and the power grid (in terms of integrating wind power). 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. 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. Considerable developments in the real-time telemetry of demand-side systems allow independent system operators (ISOs) to use reserves provided by demand response (DR) in ancillary service markets. Currently, many ISOs have designed programs to utilize the reserve provided by DR in electricity markets. This paper presents a stochastic model to schedule reserves provided by DR in the wholesale electricity markets. Demand-side reserve is supplied by demand response providers (DRPs), which have the responsibility of aggregating and managing customer responses. A mixed-integer representation of reserve provided by DRPs and its associated cost function are used in the proposed stochastic model. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer programming (SMIP) problem. The first-stage involves network-constrained unit commitment in the base case and the second-stage investigates security assurance in system scenarios. The proposed model would schedule reserves provided by DRPs and determine commitment states of generating units and their scheduled energy and spinning reserves in the scheduling horizon. The proposed approach is applied to two test systems to illustrate the benefits of implementing demand-side reserve in electricity markets. The robustness of smart grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause blackouts and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the tradeoff between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational smart grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria. One way to cope with the increasing demand in transportation networks is to integrate standard solutions with more intelligent measures. This problem has been approached from different sides such as the study of the assignment of the demand in the network, and the investigation of the effects of control measures. However, given that most of these approaches are complex and deal with different levels of abstraction of the original problem, there has been few attempts to address both demand and control in a single tool. This paper presents an overview of ITSUMO, a microscopic traffic simulator whose implementation uses agent technologies with a bottom-up philosophy in mind. We give an overview of the system and some details of its modules (simulation kernel, data management, control, driver and routing, and visualization), followed by an example of its use. I. I NTRODUCTION AND MOTIVATION