Energy big data: A survey <s> B. ENERGY DEMAND RESPONSE <s> A considerable number of institutions, companies and societies demand and trust the possibility of a sustainable world where the energetic production is based on renewable sources. Most likely future problem of the no-controllable renewable electric production (mainly wind and solar) may be the temporally variability. To follow the power demand is necessary controllable generation. The paper analyses based on hypothetical situations how to manage through storage systems the no-controllable productions to get the complete substitution of the no-renewable power plants. Data from real renewable plants has been used to explore in big electric systems (Spain) the optimal sizing of said storage systems. It is shown that with a potential renewable overproduction around 150% versus a yearly demand only a 1.5% energy storage capacity versus demand should be necessary. <s> BIB001 </s> Energy big data: A survey <s> B. ENERGY DEMAND RESPONSE <s> Demand Response Management (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists. We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies. We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability. <s> BIB002 </s> Energy big data: A survey <s> B. ENERGY DEMAND RESPONSE <s> The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Utilities are interested in enrolling small and medium sized customers that can provide demand curtailment during periods of shortfall in renewable production. It then becomes important to be able to target the right customers among the large population, since each enrollment has a cost. The availability of high resolution information about each consumers demand consumption can significantly change how such targeting is done. This paper develops a methodology for large scale targeting that combines data analytics and a scalable selection procedure. We propose an efficient customer selection method via stochastic knapsack problem solving and a simple response modeling in one example DR program. To cope with computation issues coming from the large size of data set, we design a novel approximate algorithm. <s> BIB003 </s> Energy big data: A survey <s> B. ENERGY DEMAND RESPONSE <s> Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of local power generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with local generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone. <s> BIB004
The demand of electric power consumers has been expanding along with the increasing use of machines and emerging types of appliances such as EVs and smart home furniture. Thus, the concern on the stability and reliability of electricity supply has also been aroused. However, since the traditional power grid lacks real-time response between demand and supply, it cannot meet these demands due to its inflexible distribution BIB002 . Therefore, demand response is expected to be an aspect for the future smart grid. Demand response is usually based on the classification analysis BIB003 . Users can be classified into several types according to climatic features, geographic conditions and social stratum. Targeting ar different class of users, different daily load curves of electrical equipments can be drawn. These curves indicates the electrical characteristics, including time intervals, effective factors of electricity consumption, etc. Based on the classification analysis, the total demand response from a region or a kind of users can be available through polymerization. Meanwhile, the analysis results will work in designing incentive demand response mechanism. Liu et al. BIB004 provided a power demand model which is derived from workload models and cooling demands of the data center. Specifically, they calculated the total power demand by:  Goyena and Acciona BIB001 proposed an energetic balance algorithm to warranty the electric demand in a large energy storage system. This system analyzes the measured data constantly and commits for high accuracy.In this algorithm, electric demand situations of overproduction and underproduction are distinguished. Compared with the none-renewable production, renewable production has been considered as the priority in order to meet the energy demand. According to the results of this balance algorithm, datastorage level will be increased, decreased or limited.