Short-term power system operational planning problems that consider multi-stage uncertainties pose significant challenges, not only in the design of tractable optimization frameworks for implementing them, but also in the testing and benchmarking of such frameworks. This paper presents an implementation using the open-source Matpower Optimal Scheduling Tool (MOST) to study and compare a stochastic day-ahead, security-constrained unit commitment problem with a more traditional deterministic approach. The comparison is based on a testing methodology for day-ahead plans designed to produce expected performance estimates with minimal biases from modeling assumptions. Emphasis is given in the proposed stochastic approach to explicit modeling of the operational characteristics of the technologies available, their spatial and temporal coupling, and the regulatory constraints that assure reliability and adequacy. The problem formulations and testing methodology are described and simulation results from MOST are presented, with discussion of implications for future market design. All of the code and data to replicate the simulations is provided online.

This two-part paper addresses the design of retail electricity tariffs for distribution systems with distributed energy resources such as solar power and storage. In particular, the optimal design of dynamic two-part tariffs for a regulated monopolistic retailer is considered, where the retailer faces exogenous wholesale electricity prices and fixed costs on the one hand and stochastic demands with intertemporal price dependencies on the other. Part I presents a general framework and analysis for revenue adequate retail tariffs with advanced notification, dynamic prices, and uniform connection charges. It is shown that the optimal two-part tariff consists of a dynamic price that may not match the expected wholesale price and a connection charge that distributes uniformly among all customers the retailer's fixed costs and a price-volume risk premium. A sufficient condition for the optimality of the derived two-part tariff among the class of arbitrary ex-ante tariffs is obtained. Numerical simulations quantify the substantial welfare gains that the optimal two-part tariff may bring compared to the optimal linear tariff (without connection charge). Part II focuses on the impact of two-part tariffs on the integration of distributed energy resources.

This two-part paper addresses the design of retail electricity tariffs for distribution systems with distributed energy resources (DERs). Part I presents a framework to optimize ex-ante two-part tariffs for a regulated retailer who faces stochastic wholesale prices on the one hand and stochastic demand on the other. In part II, the integration of DERs is addressed by analyzing their effect on the optimal two-part tariff and the induced welfare gains. Two DER integration models are considered: a decentralized model involving behind-the-meter DERs and a centralized model with retailer-integrated DERs. It is shown that DERs integrated under either model can achieve the same social welfare and that net metering is optimal. The retail prices for both models are equal and reflect the expected wholesale prices. The connection charges differ and are affected by the retailer's fixed costs and the statistical dependencies between wholesale prices and behind-the-meter DERs. These charges are higher under the decentralized model. An empirical analysis is presented to estimate the impact of DER on welfare distribution and cross-subsidies. It shows that, with the prevailing net-metering tariffs, consumer welfare decreases with the level of DER integration. Issues of cross-subsidy and practical drawbacks of decentralized integration are also discussed.

The decreasing cost of energy storage technologies coupled with their potential to bring significant benefits to electric power networks have kindled research efforts to design both market and regulatory frameworks to facilitate the efficient construction and operation of such technologies. In this paper, we examine an open access approach to the integration of storage, which enables the complete decoupling of a storage facility's ownership structure from its operation. In particular, we analyze a nodal spot pricing system built on a model of economic dispatch in which storage is centrally dispatched by the independent system operator (ISO) to maximize social welfare. Concomitant with such an approach is the ISO's collection of a merchandising surplus reflecting congestion in storage. We introduce a class of tradable electricity derivatives ---referred to as financial storage rights (FSRs)--- to enable the redistribution of such rents in the form of financial property rights to storage capacity; and establish a generalized simultaneous feasibility test to ensure the ISO's revenue adequacy when allocating such financial property rights to market participants. Several advantages of such an approach to open access storage are discussed. In particular, we illustrate with a stylized example the role of FSRs in synthesizing fully hedged, fixed-price bilateral contracts for energy, when the seller and buyer exhibit differing intertemporal supply and demand characteristics, respectively.

The economic efficiency of two-part retail electricity tariffs in the presence of renewable resources in the distribution system is analyzed. Two integration models are considered: (i) a centralized model involving a regulated retail utility who owns the resources as part of its generation portfolio, and (ii) a decentralized model in which each consumer individually owns and operates the resources behind the meter and is capable of selling surplus electricity back to the retailer in a netmetering setting. The structure of the optimal two-part tariffs is obtained. For both integration models, it is shown that, under two-part tariffs, renewable resources generally improve efficiency and consumer surplus. In contrast, under linear tariffs, the integration of renewable resources by consumers may lower both consumer surplus and social welfare.

The decreasing cost of energy storage technologies coupled with their potential to bring significant benefits to electric power networks have kindled research efforts to design both market and regulatory frameworks to facilitate the efficient integration of such technologies. The primary challenge resides in designing market systems that provide the correct incentives to deploy and operate storage systems efficiently in both the short and long-run. In the following paper, we propose an open access approach to the integration of storage in which storage is treated as a communal asset centrally operated by the System Operator (SO) to maximize social welfare; not unlike the operation of the transmission network today. Concomitantly, we propose a novel electricity derivative, which we refer to as financial storage rights (FSRs), to enable the redistribution of the additional merchandising surplus (attributable to storage) collected by the SO. FSRs do not interfere with the socially optimal operation of storage, and their definition as a sequence of nodal power injections facilitates their use by market participants to mitigate the cost and/or risk of meeting contractual commitments. Moreover, the revenue collected by the SO through the sale of FSRs can be used to remunerate capital expenditures in storage.

Stochastic optimization has become one of the fundamental mathematical frameworks for modeling power systems with important sources of uncertainty in the demand and supply sides. In this framework, a main challenge is to find optimal dispatch policies and settlement schemes that support a market equilibrium. In this paper, the economic dispatch under linear network constraints and resource uncertainty is revisited. Piece-wise affine continuous dispatch policies and locational prices that support a market equilibrium using a two-settlement scheme are derived. We find that the ex-post locational prices are piecewise affine continuous functions of the system uncertainties.

Based on a two-settlement electricity market model built within a stochastic programming framework, this paper proposes a market-clearing mechanism that allows flexible random participants — such as variable renewable energy resources and price-sensitive load-serving entities — to mitigate their risks of facing economic losses in the market. More precisely, the mechanism extends to flexible random participants the risk-mitigating capabilities that reserve capacity offers enable for firm generators (i.e., conventional generators). The proposed mechanism is based on the premise that flexible random participants should be remunerated for the partial control capabilities they may have over their resources in spite of their randomness.

The objective of this paper is to analyze the design of market mechanisms that provide support for the adoption of renewable energy sources (RES) into the electricity system. Once RES become a significant share of the generation portfolio, the reliable and secure operation of the electricity system is threatened. The underlying question is how to better manage the uncertainty of these RES, specifically wind, in an equitable way, so social planners can optimally operate and contract for energy and ancillary services, and make use of the available network resources, such as energy storage systems (ESS) and deferrable demand (DD). While using storage collocated with wind farms is a supply side mechanism that allows reducing the variability in outputs from RES, the use of deferrable demands actively engages the demand side of the market, , thus bringing benefits in terms of the transmission congestion observed and other system metrics, such as the amount of capacity needed to reliably cover the demand. The adoption of renewable energy sources into the electricity system provides the opportunity to reevaluate the fundamental ways in which one of the most complex engineered systems is managed. Historically, the electricity network evolved to a design in which large scale generation placed far (geographically and electrically) from demand centers produced electricity that was transmitted over a high tension Alternate Current (AC) network. Once closer to demand centers, the voltage is transformed lo lower tension levels, and a typically radial distribution system apportions the electric power for residential, industrial and commercial consumption. In this design, generation followed demand, with the yearly peak demand determining the amount of generation capacity installed in the system. ∗ajl259@cornell.edu, 415 Warren Hall, Cornell University, Ithaca, NY, 14853

A single-period day-ahead stochastic market-clearing mechanism that schedules large and Distributed Generators (DGs) to reliably supply the system demand is proposed. The mechanism considers the stochasticity of both, the system demand and the generation of variable DGs to satisfy a reliability criterion. The reserves provided by large generators, are endogenously determined by the scheduling mechanism. The mechanism allocates in a cost-reflective manner the additional operational costs introduced by variable DGs in such a way that no entry barriers are imposed to them. Aiming to achieve the effective integration of DG within the proposed mechanism, flexibility rights are defined, and a further classification of DGs according to their generation capacity is proposed.