Dynamic Pricing and Distributed Energy Management for Demand Response

The problem of dynamic pricing of electricity in a retail market is considered. A Stackelberg game is used to model interactions between a retailer and its customers; the retailer sets the day-ahead hourly price of electricity and consumers adjust real-time consumptions to maximize individual consumer surplus. For thermostatic demands, the optimal aggregated demand is shown to be an affine function of the day-ahead hourly price. A complete characterization of the trade-offs between consumer surplus and retail profit is obtained. The Pareto front of achievable trade-offs is shown to be concave, and each point on the Pareto front is achieved by an optimal day-ahead hourly price. Effects of integrating renewables and local storage are analyzed. It is shown that benefits of renewable integration all go to the retailer when the capacity of renewable is relatively small. As the capacity increases beyond a certain threshold, the benefit from renewable that goes to consumers increases.Comment: 9 page

Variable-Rate Distributed Source Coding in the Presence of Byzantine Sensors

The distributed source coding problem is considered when the sensors, or encoders, are under Byzantine attack; that is, an unknown number of sensors have been reprogrammed by a malicious intruder to undermine the reconstruction at the fusion center. Three different forms of the problem are considered. The first is a variable-rate setup, in which the decoder adaptively chooses the rates at which the sensors transmit. An explicit characterization of the variable-rate minimum achievable sum rate is stated, given by the maximum entropy over the set of distributions indistinguishable from the true source distribution by the decoder. In addition, two forms of the fixed-rate problem are considered, one with deterministic coding and one with randomized coding. The achievable rate regions are given for both these problems, with a larger region achievable using randomized coding, though both are suboptimal compared to variable-rate coding.Comment: 5 pages, submitted to ISIT 200

Renewables and Storage in Distribution Systems: Centralized vs. Decentralized Integration

The problem of integrating renewables and storage into a distribution network is considered under two integration models: (i) a centralized model involving a retail utility that owns the integration as part of its portfolio of energy resources, and (ii) a decentralized model in which each consumer individually owns and operates the integration and is capable of selling surplus electricity back to the retailer in a net-metering setting. The two integration models are analyzed using a Stackelberg game in which the utility is the leader in setting the retail price of electricity, and each consumer schedules its demand by maximizing individual consumer surplus. The solution of the Stackelberg game defines the Pareto front that characterizes fundamental trade-offs between retail profit of the utility and consumer surplus. It is shown that, for both integration models, the centralized integration uniformly improves retail profit. As the level of integration increases, the proportion of benefits goes to the consumers increases. In contrast, the consumer-based decentralized integration improves consumer surplus at the expense of retail profit of the utility. For a profit regulated utility, the consumer based integration may lead to smaller consumer surplus than that when no renewable or storage is integrated at either the consumer or the retailer end.Comment: 10 page

Capacity of Cooperative Fusion in the Presence of Byzantine Sensors

The problem of cooperative fusion in the presence of Byzantine sensors is considered. An information theoretic formulation is used to characterize the Shannon capacity of sensor fusion. It is shown that when less than half of the sensors are Byzantine, the effect of Byzantine attack can be entirely mitigated, and the fusion capacity is identical to that when all sensors are honest. But when at least half of the sensors are Byzantine, they can completely defeat the sensor fusion so that no information can be transmitted reliably. A capacity achieving transmit-then-verify strategy is proposed for the case that less than half of the sensors are Byzantine, and its error probability and coding rate is analyzed by using a Markov decision process modeling of the transmission protocol.Comment: 8 pages, 2 figure

Distributed Learning and Multiaccess of On-Off Channels

The problem of distributed access of a set of N on-off channels by K<N users is considered. The channels are slotted and modeled as independent but not necessarily identical alternating renewal processes. Each user decides to either observe or transmit at the beginning of every slot. A transmission is successful only if the channel is at the on state and there is only one user transmitting. When a user observes, it identifies whether a transmission would have been successful had it decided to transmit. A distributed learning and access policy referred to as alternating sensing and access (ASA) is proposed. It is shown that ASA has finite expected regret when compared with the optimal centralized scheme with fixed channel allocation.Comment: 8 pages, 5 figure

On the Dynamics of Distributed Energy Adoption: Equilibrium, Stability, and Limiting Capacity

The death spiral hypothesis in electric utility represents a positive feedback phenomenon in which a regulated utility is driven to financial instability by rising prices and declining demand. We establish conditions for the existence of death spiral and conditions of stable adoption of distributed energy resources. We show in particular that linear tariffs always induce death spiral when the fixed operating cost of the utility rises beyond a certain threshold. For two-part tariffs with connection and volumetric charges, the Ramsey pricing that optimizes myopically social welfare subject to the revenue adequacy constraint induces a stable equilibrium. The Ramsey pricing, however, inhibits renewable adoption with a high connection charge. In contrast, a two-part tariff with a small connection charge results in a stable adoption process with a higher level of renewable adoption and greater long-term total consumer surplus. Market data are used to illustrate various solar adoption scenarios.Comment: 13 pages, 13 figure

Stochastic Interchange Scheduling in the Real-Time Electricity Market

The problem of multi-area interchange scheduling in the presence of stochastic generation and load is considered. A new interchange scheduling technique based on a two-stage stochastic minimization of overall expected operating cost is proposed. Because directly solving the stochastic optimization is intractable, an equivalent problem that maximizes the expected social welfare is formulated. The proposed technique leverages the operator's capability of forecasting locational marginal prices (LMPs) and obtains the optimal interchange schedule without iterations among operators

Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions

We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for $K$ goods at each period. As a bidding strategy, we propose a polynomial-time algorithm, inspired by the dynamic programming approach to the knapsack problem. The proposed algorithm, referred to as dynamic programming on discrete set (DPDS), achieves a regret order of $O(\sqrt{T\log{T}})$. By showing that the regret is lower bounded by $\Omega(\sqrt{T})$ for any strategy, we conclude that DPDS is order optimal up to a $\sqrt{\log{T}}$ term. We evaluate the performance of DPDS empirically in the context of virtual trading in wholesale electricity markets by using historical data from the New York market. Empirical results show that DPDS consistently outperforms benchmark heuristic methods that are derived from machine learning and online learning approaches

Maximum Likelihood Fusion of Stochastic Maps

The fusion of independently obtained stochastic maps by collaborating mobile agents is considered. The proposed approach includes two parts: matching of stochastic maps and maximum likelihood alignment. In particular, an affine invariant hypergraph is constructed for each stochastic map, and a bipartite matching via a linear program is used to establish landmark correspondence between stochastic maps. A maximum likelihood alignment procedure is proposed to determine rotation and translation between common landmarks in order to construct a global map within a common frame of reference. A main feature of the proposed approach is its scalability with respect to the number of landmarks: the matching step has polynomial complexity and the maximum likelihood alignment is obtained in closed form. Experimental validation of the proposed fusion approach is performed using the Victoria Park benchmark dataset.Comment: 10 pages, 8 figures, submitted to IEEE Transactions on Signal Processing on 24-March-201

On Robust Tie-line Scheduling in Multi-Area Power Systems

The tie-line scheduling problem in a multi-area power system seeks to optimize tie-line power flows across areas that are independently operated by different system operators (SOs). In this paper, we leverage the theory of multi-parametric linear programming to propose algorithms for optimal tie-line scheduling within a deterministic and a robust optimization framework. Through a coordinator, the proposed algorithms are proved to converge to the optimal schedule within a finite number of iterations. A key feature of the proposed algorithms, besides their finite step convergence, is the privacy of the information exchanges; the SO in an area does not need to reveal its dispatch cost structure, network constraints, or the nature of the uncertainty set to the coordinator. The performance of the algorithms is evaluated using several power system examples