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Prosumers with generation and storage capabilities can supply en- ergy back to the grid, or trade their surplus with other prosumers for their mutual benefit. A prosumer aggregation that facilitates such trades will price the energy being traded to achieve an objective such as profit maximization, social welfare, or market equilibrium. We propose the use of reinforcement learning to design a trans- active controller to price energy in a prosumer aggregation. This has an advantage over other decentralized pricing mechanisms as it does not rely on iterative price settlement or load estimation by prosumers, and estimates the price in a day ahead manner. We present numerical case studies to evaluate our controller, and dis- cuss extensions to implement this in real prosumer aggregations. 

The value created by aggregating behind-the-meter distributed energy storage devices for grid services depends on how much storage is in the system and the power network operation conditions. To understand whether market-driven distributed storage investment will result in a socially desirable outcome, we formulate and analyze a network storage investment game. By explicitly characterizing the set of Nash equilibria (NE) for two examples, we establish that the uniqueness and efficiency of NE depend critically on the power network conditions. Furthermore, we show it is guaranteed that NE support social welfare for general power networks, provided we include two modifications in our model. These modifications suggest potential directions for regulatory interventions. 

Frequency regulation is crucial for balancing the supply and demand of modern electricity grids. To provide regulation services, it is important to understand the capability of flexible resources to track regulation signals. This paper studies the problem of submitting capacity bids to a forward regulation market based on historical regulation signals. We consider an aggregator who manages a group of flexible resources with linear dynamic constraints. He seeks to find the optimal capacity bid, so that real-time regulation signals can be followed with an arbitrary guaranteed probability. We formulate this problem as a chance-constrained program with unknown regulation signal distributions. A sampling and discarding algorithm is proposed. It provably provides near-optimal solutions at a guaranteed probability of success without knowing the distribution of the regulation signals. This result holds for resources with arbitrary linear dynamics and allows arbitrary intra-hour data correlations. We validate the proposed algorithm with real data via numerical simulations. Two cases are studied: (1) CAISO market, where providers separately submit capacity estimates for regulation up and regulation down signals, (2) PJM market, where regulation up and down capacities are the same. Simulation results show that the proposed algorithm provides near-optimal capacity estimates for both cases. 

By arbitraging among consumer comfort margins, buildings energy consumption can be changed by providing flexibility to grids. To manipulate the buildings energy consumption, a new contract-based approach to for multi-zone building heating, ventilation and air-conditioning (HVAC) systems is proposed. The approach includes the real-time markets by changing buildings optimal consumption pattern based on triggers sent by the aggregator. Also to decrease the energy consumption of buildings, the user is allowed to select the time-slots and rewards are provided to the user for aggregating flexibility. The aggregator bundles flexibility from the buildings at different time-slots and sells in real-time markets. The idea in aggregator's problem is to maximize aggregator's profits by selling flexibility in real-time markets (RTM) while ensuring the provisioning of flexibility from the buildings through incentives. To address this problem, we formulate it as a distributed optimization problem and then provide a method to solve it which provides good scalability, a requirement for large commercial buildings with multiple zones to participate in RTM. We illustrate the scalability and performance of the contract-based approach and solution technique in a building with 200 zones. Also, user participation based on their time-preferences is included in the proposed optimization. Finally, a scalable technique is shown which can be adopted in existing building automation systems. 

In the classical risk limiting dispatch (RLD) formulation, the system operator dispatches generators relying on information about the distribution of demand. In practice, such information is not readily available and therefore is estimated using historical demand and auxiliary information (or features) such as weather forecasts. In this paper, instead of using a separated estimation and optimization procedure, we propose learning methods that directly compute the RLD decision rule based on historical data. Using tools from statistical learning theory, we then develop generalization bounds and sample complexity results of the proposed methods. These algorithms and performance guarantees, developed for the single-bus network, are then extended to a general network setting for the uniform reserve case. 

This paper studies rooftop solar photovoltaic (PV) investment decisions of households. Two cases are considered: (a) the status quo of net-metering, and (b) a new sharing economy model. Under net-metering, households can sell back their excess generation to the utility at their retail tariff subject to the prevalent constraint that they cannot be net producers of electricity on an annual basis. In our sharing economy model, households can pool their excess PV generation and trade it in a spot market among themselves, but the collective cannot sell electricity back to the utility. Our objective in studying these two cases is that net-metering programs are under threat and being phased out, which places future residential PV investment at risk. In the event of this contingency, we argue that the sharing economy model offers a pathway to preserve and even accelerate residential PV investment. We derive expressions for the optimal investment decisions in each case assuming that households are rational and wish to minimize their costs. We characterize the random clearing price in the spot market for excess PV generation under the sharing model. We show that the optimal investment decisions are determined by a simple threshold policy. Households whose PV productivity metric exceeds this threshold invest the maximum possible, while those that fall below the threshold do not invest. We offer a convergent algorithm to compute this threshold. We close with a small-scale simulation study that reveals the favorable properties of the sharing economy model for residential PV investments. 

This paper proposes an Automatic Power Ex- change (APEX) that enables monetization of underutilized distribution system energy resources. APEX features an open- gate forward market design to incorporate uncertainty from variable resources, and an explicit flexibility market that schedules flexible resources based on information submitted by users through a simple yet expressive order format. We study the non-convex non-preemptive scheduling problem in APEX, proposing polynomial time algorithms with finite and asymptotic performance guarantees. We then analyze the prop- erties of marginal pricing, generalized to fit the APEX context with forward markets and distribution network constraints. We establish that it is revenue adequate but may lead to inadmissible prices for flexible orders. We then suggest a simple pricing mechanism that provably produces admissible prices for users and adequate revenue for APEX if implemented together with the proposed scheduling algorithms. 

Demand Response (DR) programs serve to reduce the demand for electricity at times when the supply is scarce and expensive. Consumers or agents with flexible consumption profiles are recruited by an aggregator who manages the DR program. These agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates against which load reductions are measured. Baseline consumption and the true cost of load reduction are consumer specific parameters and are unknown to the aggregator. The key components of any DR program are: (a) establishing a baseline against which demand reduction is measured, (b) designing the payment scheme for agents who reduce their consumption from this baseline, and (c) the selection scheme. We propose a self-reported baseline mechanism (SRBM) for the DR program. We show that truthful reporting of baseline and marginal utility is both incentive compatible and individually rational for every consumer under SRBM. We also give a a pod-sorting algorithm based DR scheduling for selecting consumers that is nearly optimal in terms of expected cost of DR provision. 

The sharing economy has upset the market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers. Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow’s Smart Grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas.