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.
The approaches for demand side management focus on different levels of the power system. On the grid operator level, studies focus for example on the minimization of power flow fluctuations @cite_17 or the integration of renewable energy @cite_13 . The distribution grid operator uses consumption forecasts to balance grids with a high penetration of decentralized generation of renewable energy (e.g., @cite_30 , @cite_14 ). Other studies look at the level of groups of consumers with a focus on game theoretic frameworks @cite_27 or virtual price signals @cite_6 . Most work on demand response, however, focuses on the level of end consumers. Recent research has studied the use of variable price signals for individual customers. These dynamic tariffs penalize consumption during certain periods of time with increased electricity prices, so that customers can respond by adjusting their consumption (e.g., @cite_8 , @cite_7 ). However, @cite_1 points out that demand side management with variable price signals can cause instabilities through load synchronization. To avoid uncontrolled behavior, accurate consumption forecasts can help utilities to select customers that are most suitable for a demand response program.
@cite_30 @cite_14 @cite_7 @cite_8 @cite_1 @cite_6 @cite_27 @cite_13 @cite_17
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The increased uncertainty of the electric grid due to the penetration of renewable energy sources and deregulation of the electric market is aimed to be alleviated by demand response (DR) in the future smart grid. The demand-side resources can be incentivized to alter their consumption patterns by varying their electricity price over time. A major residential energy demand contribution is from electric heating, which, when combined with smart energy storage using water heaters, could be utilized to defer consumption to more inexpensive periods without affecting the customer's thermal quality of service. The objective is to optimize the consumer electricity price of electric storage space heating customers, in order to maximize the profit of the retailer. This approach of varying the customer electricity prices leads to a game-theoretic scenario, where the procurement and consumption profiles of the retailer and consumer agents are based on the set electricity price. The optimization of the consumer electricity price is shown to offer lesser expense for the retailer. In addition, hourly load-following can be improved by offering further discounts for the consumers. 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. The integration of renewable energy systems (RESs) in smart grids (SGs) is a challenging task, mainly due to the intermittent and unpredictable nature of the sources, typically wind or sun. Another issue concerns the way to support the consumers' participation in the electricity market aiming at minimizing the costs of the global energy consumption. This paper proposes an energy management system (EMS) aiming at optimizing the SG's operation. The EMS behaves as a sort of aggregator of distributed energy resources allowing the SG to participate in the open market. By integrating demand side management (DSM) and active management schemes (AMS), it allows a better exploitation of renewable energy sources and a reduction of the customers' energy consumption costs with both economic and environmental benefits. It can also improve the grid resilience and flexibility through the active participation of distribution system operators (DSOs) and electricity supply demand that, according to their preferences and costs, respond to real-time price signals using market processes. The efficiency of the proposed EMS is verified on a 23-bus 11-kV distribution network. 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. 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. 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). 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. The emergence of cloud computing has established a trend towards building energy-hungry and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric power grid by significantly increasing the load at locations where they are built. Dynamic energy pricing policies in the recently proposed smart grid technology can incentivize the cloud computing controller to shift their computation load towards data centers in regions with cheaper electricity. On the other hand, distributed data centers also provide opportunities to help the smart grid to improve load balancing and robustness. To shed some light into these opportunities, this paper considers an interaction system of the smart grid, the cloud computing system, and other load devices. A nested two stage game based formulation is proposed based on the location-dependent real-time pricing policy of the smart grid. The leading player in this game is the smart grid controller that announces the relationship between the electricity price at each power bus and the total load demand at that bus. In the second stage, the cloud computing controller performs resource allocation as response to the pricing functions, whereas the other load devices perform demand side management. The objective of the smart grid controller is to maximize its own profit and perform load balancing among power buses, whereas the objective of the cloud computing controller is to maximize its own profit with respect to the location-dependent pricing functions. The optimal strategies are derived based on the backward induction principle for the smart grid controller, the cloud computing controller, and the other load devices, using convex optimization and heuristic search. Energy management is becoming a crucial issue in the future power grid system as more controllable energy resources and responsive loads with communications abilities are being introduced into the smart grid. This paper proposes a novel distributed approach to deal with energy management in the smart grid under dispatchable distributed generators and responsive loads using real-time pricing (RTP) and consensus networks to maximize the social welfare. In our algorithm, each distributed generation consumer unit, in response to the local price of energy, decides on its optimal power generation consumption level to maximize its benefit at the device level. However, the consensus-based coordination of price among local retailers drives the behavior of the overall system toward the global optimum, despite the greedy behavior of the generation and consumer units. The main features of our algorithm are computational and communicational scalability, as well as privacy of information. 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. 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. In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies. Demand response (DR) programs encourage end-use customers to alter their power consumption in response to DR events such as change in real-time electricity prices. Facilitating household participation in DR programs is essential as the residential sector accounts for a sizable portion of the total energy consumed. However, manually tracking energy prices and deciding on how to schedule home appliances can be a challenge for residential consumers who are accustomed to fixed price electricity taris. In this work, we present Yupik, a system that helps users respond to real-time electricity prices while being sensitive to their context and lifestyle. Yupik combines sensing, analytics, and optimization to generate appliance usage schedules that may be used by households to minimize their energy bill as well as potential lifestyle disruptions. Yupik uses jPlugs, appliance level energy metering devices, to continuously monitor the power usage by various home appliances. The consumption patterns as well as data from external sources are analyzed using data mining algorithms to infer user's preferred usage profile. Using the preferred profile as a reference, Yupik's optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupik's capabilities are demonstrated with the help of preliminary data collected from a home that was instrumented with jPlugs to monitor the power usage of a few devices. 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. In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider. 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. 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. From the perspective of global warming mitigation and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all electric apartment houses or residence such as dc smart houses are increasing. However, due to the fluctuating power from renewable energy sources and loads, supply-demand balancing of power system becomes problematic. The smart grid is a solution to this problem. This paper presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuation. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuation, it is possible to reduce the electric power consumption and the cost of electricity. This system consists of a photovoltaic generator, heat pump, battery, solar collector, and load. To verify the effectiveness of the proposed system, results are used in simulation presented. Smart pricing methods using auction mechanism allow more information exchange between users and providers, and they can meet users' energy demand at a low cost of grid operation, which contributes to the economic and environmental benefit in smart grid. However, when asked to report their energy demand, users may have an incentive to cheat in order to consume more while paying less, causing extra costs for grid operation. So it is important to ensure truthfulness among users for demand side management. In this paper, we propose an efficient pricing method that can prevent users' cheating. In the proposed model, the smart meter can record user's consumption information and communicate with the energy provider's terminal. Users' preferences and consumption patterns are modeled in form of a utility function. Based on this, we propose an enhanced AGV (Arrow-d'Aspremont-Gerard-Varet) mechanism to ensure truthfulness. In this incentive method, user's payment is related to its consumption credit. One will be punished to pay extra if there is a cheat record in its consumption history. We prove that the enhanced AGV mechanism can achieve the basic qualifications: incentive compatibility, individual rationality and budget balance. Simulation results confirm that the enhanced AGV mechanism can ensure truth-telling, and benefit both users and energy providers. We consider the problem of forecasting the aggregate demand of a pool of price-responsive consumers of electricity. The response of the aggregate load to price is modeled by an optimization problem that is characterized by a set of marginal utility curves and minimum and maximum power consumption limits. The task of estimating these parameters is addressed using a generalized inverse optimization scheme that, in turn, requires solving a nonconvex mathematical program. We introduce a solution method that overcomes the nonconvexities by solving instead two linear problems with a penalty term, which is statistically adjusted by using a cross-validation algorithm. The proposed methodology is data-driven and leverages information from regressors, such as time and weather variables, to account for changes in the parameter estimates. The power load of a group of heating, ventilation, and air conditioning systems in buildings is simulated, and the results show that the aggregate demand of the group can be successfully captured by the proposed model, making it suitable for short-term forecasting purposes.