text: 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.
  @cite_30: 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.
  @cite_14: The purpose of this paper is to quantify the potential for plug-in electric vehicles (PEVs) to meet operating reserve requirements associated with increased deployment of wind and solar generation. The paper advances prior PEV estimates in three key ways. First, we employ easily implementable scheduling strategies with very low centralized computing requirements. Second, we estimate PEV availability based on data sampled from the National Household Travel Survey (NHTS). Third, we predict regulation demand on a per minute basis using published models from the California ISO for 20 and 33 renewable electricity supply. Our key results are as follows: First, the amount of regulation up and regulation down energy delivered by PEVs can be balanced by using a hybrid of two scheduling strategies. Second, the percentage of regulation energy that can be delivered with PEVs is always significantly higher than the percentage of conventional regulation power capacity that is deferred by PEVs. Third, regulation up is harder to satisfy with PEVs than regulation down. Fourth, the scheduling strategies we employ here have a limited impact on load following requirements. Our results indicate that 3 million PEVs could satisfy a significant portion-but not all-of the regulation energy and capacity requirements that are anticipated in California in 2020.
  @cite_7: This paper summarizes the results from an exploratory analysis of residential customer response to a critical peak pricing (CPP) experiment in California, in which 15 times per year participating customers received high price signals dispatched by a local electricity distribution company. The high prices were about three times the on-peak price for the otherwise applicable time-of-use rate. Using hourly load data collected during the 15-month experiment, we find statistically significant load reduction for participants both with and without automated end-use control technologies. During 5-h critical peak periods, participants without control technology used up to 13 less energy than they did during normal peak periods. Participants equipped with programmable communicating thermostats used 25 and 41 less for 5 and 2h critical events, respectively. Thus, this paper offers convincing evidence that the residential sector can provide substantial contributions to retail demand response, which is considered a potential tool for mitigating market power, stabilizing wholesale market prices, managing system reliability, and maintaining system resource adequacy.
  @cite_8: 
  @cite_1: Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17 and carbon emissions by up to 6 . We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).
  @cite_6: A key challenge to create a sustainable and energy-efficient society is in making consumer demand adaptive to energy supply, especially renewable supply. In this paper, we propose a partially-centralized organization of consumers, namely, a consumer cooperative for purchasing electricity from the market. We propose a novel multiagent coordination algorithm to shape the energy consumption of the cooperative. In the cooperative, a central coordinator buys the electricity for the whole group and consumers make their own consumption decisions based on their private consumption constraints and preferences. To coordinate individual consumers under incomplete information, we propose an iterative algorithm in which a virtual price signal is sent by the coordinator to induce consumers to shift demand. We prove that our algorithm converges to the central optimal solution. Additionally we analyze the convergence rate of the algorithm via simulations on randomly generated instances. The results indicate scalability with respect to the number of agents and consumption slots.
  @cite_27: 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.
  @cite_13: 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).
  @cite_17: 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.
summary: 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.