In this paper, we propose a DC optimal power flow (OPF) framework for storage portfolio optimization in transmission-constrained power networks. In particular, this model is designed to investigate two problems: 1) optimizing storage operation and allocation over a network given a fixed technology portfolio and 2) optimizing the storage portfolio (i.e., the size, technology, and network allocation of these resources). We demonstrate this framework using case studies based on the IEEE 14-bus test system with four different storage technologies. Our results show that although certain technologies are generally classified as being suitable for either power or energy services, many technologies can add value to the system by performing both fast-time scale regulation (power) and load-shifting (energy) services. These results suggest that limiting the type of service that a certain technology is compensated for may result in inefficiencies at the system level and under-valuation of storage.

This paper analyzes different models for evaluating investments in energy storage systems (ESS) in power systems with high penetration of renewable energy sources. First of all, two methodologies proposed in the literature are extended to consider ESS investment: a unit commitment model that uses the “system states” (SS) method of representing time; and another one that uses a “representative periods” (RP) method. Besides, this paper proposes two new models that improve the previous ones without a significant increase of computation time. The enhanced models are the “system states reduced frequency matrix” model which addresses short-term energy storage more approximately than the SS method to reduce the number of constraints in the problem, and the “representative periods with transition matrix and cluster indices” (RP-TM&CI) model which guarantees some continuity between representative periods, e.g., days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as benchmark. While both system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The RP-TM&CI model, on the other hand, succeeds in approximating both short- and long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.

This paper proposes a bilevel model to assist a generation company in making its long-term generation capacity investment decisions considering uncertainty regarding the investments of the other generation companies. The bilevel formulation allows for the uncoupling of investment and generation decisions, as investment decisions of the single investing generation company are taken in the upper level with the objective to maximize expected profits and generation decisions by all companies are considered in the lower level. The lower level represents the oligopolistic market equilibrium via a conjectured-price response formulation, which can capture various degrees of strategic market behavior like perfect competition, the Cournot oligopoly, and intermediate cases.

In medium- and long-term power system models, it is a common approach to approximate the demand curve by load levels in order to make the models computationally tractable. However, in such an approach, the chronological information between individual hours is lost. In this paper, we propose a novel approach to power system models which constitutes an alternative to the traditional load levels. In particular, we introduce the concept of system states as opposed to load levels, which allows us to better incorporate chronological information in power system models, thereby resulting in a more accurate representation of system outcomes such as electricity prices and total cost. Moreover, the system states can be defined taking into account various important system features at once, as opposed to load levels which are defined using just one specific feature, i.e., demand or net demand. Therefore the system states approach better captures other results such as reserve prices, which are not driven by the usual feature used to define load levels. In a case study, we compare the newly proposed methodology to a standard load level approach, which validates that the system states approach better captures power system outcomes.

Charging schedules of plug-in electric vehicles (PEVs) coordinated by an aggregating agent may increase system efficiency of allocating generation, transmission and distribution resources. Decentralized self-scheduling and local charging control appear to be preferred by vehicle manufacturers and PEV drivers who are simultaneously concerned about the longevity and reliability of their energy storage systems. In such a setting, the aggregator would have to determine energy retail prices as means to indirect load control. This paper proposes a mathematical program with equilibrium constraints optimizing the aggregator's decisions. It endogenously determines the profit-optimal price level subject to the cost minimizing charging schedule of the final customers, who are reacting to a combination of retail price signals and distribution use-of-system network charges. This active response follows an affine demand-price relationship, which is individually parametrized for each vehicle by local information of vehicle characteristics and mobility pattern. The proposed program is applied to two cases: 1) a small case study with 3 vehicles, which highlights the model functionality with detailed hourly information per vehicle; 2) a large-scale fleet of 1000 vehicles provides insights on computational burden. Numerical results indicate that adequate competition in the retail market is necessary to limit the aggregator's monopolistic profitability. Finally, sensitivity runs show dependency on the individual's willingness to pay, the cost of alternative fueling opportunities and minimum state-of-charge requirements.