Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.

Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role in unit commitment, economic dispatch, and storage system management. However, forecasting solar PV power in high temporal resolution such as five-minute resolution is challenging because most of PV forecasting models can only achieve the same temporal resolution as their predictors(i.e., weather variables), whose temporal resolution is usually low (i.e., hourly). To address this challenge, similarity-based forecasting models (SBFMs) are advocated in this paper to forecast PV power in high temporal resolution using low temporal resolution weather variables. To effectively generalize the model for different scenarios of available weather data, three forecasting models (i.e., basic SBFM, categorical SBFM, and hierarchical SBFM) are proposed. As a case study, the PV power generated by the solar panels on the rooftop of a commercial building is forecasted for the next day with a five-minute resolution under three different scenarios of available weather data. The leave-one-out cross-validation analysis reveals that using only two or three weather variables, the proposed SBFMs can achieve higher forecasting accuracy than several benchmark models.

This paper discusses a simulation-based generator-level stability index and demonstrates the use of the index to quantify the impact of different load scenarios, transmission line reactances, and generator parameters on transient stability. A Basin Stability concept is used that is based on recent works in the physics community on complex systems and power systems, which estimates the regions of attraction through time-domain simulation. The quantitative results generated by Basin Stability can provide a general ranking metric to reflect which generator or a group of generators is likely to become unstable after disturbances. A detailed sub-transient model with sixth-order sub-transient generators, second-order exciters, and third-order power system stabilizers has been used for discussion. Additionally, a multi-node basin stability study on a 16-machine 68-bus system with the swing-equation model is also included to demonstrate the replicability. The state-of-the-art parallel computing techniques can significantly reduce the simulation time of the proposed method.

This paper investigates the finite-time exponential stability analysis and stabilization problem of discrete-time switched nonlinear systems without stable subsystems. In the stability analysis, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate nonlinear subsystems. With two level functions, namely, crisp switching functions and local fuzzy weighting functions, we introduce switched fuzzy systems with approximation errors, which inherently contain both the features of the switched systems and T-S fuzzy systems. By constructing the “decreasing-jump” piecewise Lyapunov-like functions and minimum dwell time technique, a finite-time exponential stability of switched fuzzy systems with bounded approximation errors is obtained. Then, based on the finite-time exponential stability, a multiobjective evolution algorithm (nondominated sorting genetic algorithm, NSGA-II), which considers two conflicting objectives, such as the average convergence error and the average switching cost, is proposed to generate tradeoff switching sequences to stabilize the discrete-time switched nonlinear systems over a finite-time interval. A numerical example and a practical example are provided to illustrate the effectiveness of the stability and the algorithm, respectively.

In order to understand the impact of individual communities on global sustainability, we need a community sustainability assessment system (CSAS). While many sustainability assessment systems exist, they prove inadequate to the task. This article presents the results of a systematic review of the literature on existing sustainability assessment systems; offers a definition of a sustainable community; provides a multi-scale, systems approach to thinking about community; and makes recommendations from the field of performance measurement for the construction of a CSAS.

With the increasing number of controllable devices in the grid, devices owned by different entities are supposed to cooperatively support the grid operation. To balance the various interests of the customers and the utility, this paper proposes a two-level scheduling algorithm to address voltage regulation by coordinating the customer-owned battery energy storage system (BESS) and utility-owned load tap changers (LTCs) in power distribution systems. The objective is to find the optimal operation scheduling for both BESSs and LTCs that simultaneously maximize the economic payback and satisfy system operation rules. For BESSs, a local optimization problem is formulated in a chance-constrained manner in major consideration of the economic performance. For the utility interest, a Markov decision process (MDP) and a computationally efficient reinforcement learning (RL) algorithm are utilized to solve the optimal tap setting problem. To coordinate BESSs and LTCs, a global voltage adjustment is applied. The effectiveness of the proposed algorithm is validated via simulations on the IEEE 13-node and 34-node distribution test feeders.