Enabling Privacy-preserving Auctions in Big Data

We study how to enable auctions in the big data context to solve many upcoming data-based decision problems in the near future. We consider the characteristics of the big data including, but not limited to, velocity, volume, variety, and veracity, and we believe any auction mechanism design in the future should take the following factors into consideration: 1) generality (variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness and verifiability (veracity). In this paper, we propose a privacy-preserving construction for auction mechanism design in the big data, which prevents adversaries from learning unnecessary information except those implied in the valid output of the auction. More specifically, we considered one of the most general form of the auction (to deal with the variety), and greatly improved the the efficiency and scalability by approximating the NP-hard problems and avoiding the design based on garbled circuits (to deal with velocity and volume), and finally prevented stakeholders from lying to each other for their own benefit (to deal with the veracity). We achieve these by introducing a novel privacy-preserving winner determination algorithm and a novel payment mechanism. Additionally, we further employ a blind signature scheme as a building block to let bidders verify the authenticity of their payment reported by the auctioneer. The comparison with peer work shows that we improve the asymptotic performance of peer works' overhead from the exponential growth to a linear growth and from linear growth to a logarithmic growth, which greatly improves the scalability

Demand management for home energy networks using cost-optimal appliance scheduling

This paper uses problem decomposition to show that optimal dynamic home energy prices can be used to reduce the cost of supplying energy, while at the same time reducing the cost of energy for the home users. The paper makes no specific recommendations on the nature of energy pricing, but shows that energy prices can normally be found that not only result in optimal energy consumption schedules for the energy provider's problem and are economically viable for the energy provider, but also reduce total users energy costs. Following this, the paper presents a heuristic real-time algorithm for demand management using home appliance scheduling. The presented algorithm ensures users' privacy by requiring users to only communicate their aggregate energy consumption schedules to the energy provider at each iteration of the algorithm. The performance of the algorithm is evaluated using a comprehensive probabilistic user demand model which is based on real user data from energy provider E.ON. The simulation results show potential reduction of up to 17% of the mean peak-to-average power estimate, reducing the user daily energy cost for up to 14%

Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted

An Economic Analysis of Pervasive, Incentive-Based Demand Response

Demand response (DR) emerges as one of the cheapest and greenest solutions to match supply and demand in the electricity sector. While DR has been focused on large and industrial consumers, pervasive implementation (by including residential consumers) is needed to maximize its potential. This paper presents theoretical analysis of pervasive, incentive-based DR from the economics perspective. Our analysis consider cases whether (1) DR is used to encourage consumers to decrease or increase their demand, and (2) utility companies have access to a single or multiple energy sources. We determine the necessary conditions and derive the optimal incentives to benefit from DR events

Effective Consumption Scheduling for Demand-Side Management in the Smart Grid using Non-Uniform Participation Rate

Periods of peak consumer demand in today’s electricity sector are expensive to satisfy and can be the source of power failures. One possible solution is the use of demand-side management (DSM) applying dynamic pricing mechanisms. However, instead of reducing peak loads, these mechanisms can lead to peak-shifting due to the herding effect of consumers’ load-shifting behavior. To overcome this problem, we explore strategies of assigning (non-uniform) participation rates to consumers. We use a generic method to find a near-optimal distribution setting for participation rates. Our method allows DSM designers to tune the system toward consumer convenience. This means less frequent consumption schedule changes, in the price of system performance. In addition, consumers do not need to reveal their detailed consumption schedules (hence, their privacy is preserved). Using experiments, we show the impact of the herding effect and evaluate the effectiveness of the proposed solution. We thereby demonstrate price fairness for consumers. Finally, we apply our solution to a more realistic environment – one where consumers change their consumption behavior every day

When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies have investigated baseline accuracy and bias for large, industrial and commercial customers. However, the analysis of baseline performance for residential customers has received less attention. In this paper, we analyze DR baselines for residential customers. Our analysis goes beyond accuracy and bias by understanding the impact of baselines on all stakeholders’ profit. Using our customer models, we successfully show how customer participation changes depending on the incentive actually received. We found that, in general, bias is more relevant than accuracy for determining which baseline provides the highest profit to stakeholders. Consequently, this result provides a valuable insight into designing effective DR incentive schemes

Matching Demand with Supply in the Smart Grid using Agent-Based Multiunit Auction

Abstract—Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads. However, most of these proposals rely on consumer’s willingness to act. In this paper, we propose an approach to cut PAR explicitly from the supply side. The resulting cut loads are then distributed among consumers by the means of a multiunit auction which is done by an intelligent agent on behalf of the consumer. This approach is also in line with the future vision of the smart grid to have the demand side matched with the supply side. Experiments suggest that our approach reduces overall system cost and gives benefit to both consumers and the energy provider. I

Pervasive Data Analytics for Sustainable Energy Systems

With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates efforts to balance supply and demand, since their generation is intermittent and unpredictable. Traditionally, it has always been the supply side that has adapted to follow energy demand, however, in order to have a sustainable energy system for the future, the demand side will have to be better managed to match the available energy supply. In the first part of this thesis, we focus on understanding customers' energy consumption behavior (demand analytics). While previously, information about customer's energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we then develop a versatile customer segmentation framework, track cluster changes over time, and identify key characteristics that define a cluster. Additionally, although household-level consumption is hard to predict, it can be used to improve aggregate-level forecasting by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the customer base. Furthermore, we develop an approach to model the uncertainty of future demand. In contrast to previous work that used computationally expensive methods, such as simulation, bootstrapping, or ensemble, we construct prediction intervals directly using the time-varying conditional mean and variance of future demand. While analytics on customer energy data are indeed essential to understanding customer behavior, they could also lead to breaches of privacy, with all the attendant risks. The first part of this thesis closes by exploring symbolic representations of smart meter data which still allow learning algorithms to be performed on top of them, thus providing a trade-off between accurate analytics and the protection of customer privacy. In the second part of this thesis, we focus on mechanisms for incentivizing changes in customers' energy usage in order to maintain (electricity) grid stability, i.e., Demand Response (DR). We complement previous work in this area (which typically targeted large, industrial customers) by studying the application of DR to residential customers. We first study the influence of DR baselines, i.e., estimates of what customers would have consumed in the absence of a DR event. While the literature to date has focused on baseline accuracy and bias, we go beyond these concepts by explaining how a baseline affects customer participation in a DR event, and how it affects both the customer and company profit. We then discuss a strategy for matching the demand side with the supply side by using a multiunit auction performed by intelligent agents on behalf of customers. The thesis closes by eliciting behavioral incentives from the crowd of customers for promoting and maintaining customer engagement in DR programs