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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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. 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 (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off- peak times, the utility company charges consumers the differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off- peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested and foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50 compared to existing DSM strategies. 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.