A Vision for Co-optimized T&D System Interaction with Renewables and Demand Response

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. The use of demand response to counterbalance the intermittency of renewable generation brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. In this paper, a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system is described. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the microgrid can also sell (buy) excess (necessary) energy from the transmission system. Preliminary work explores price mechanisms to manage the microgrid and its interactions with the transmission system

Co-optimizing High and Low Voltage Systems: Bi-Level vs. Single-Level Approach

This paper presents a bi-level optimization framework applied to optimize system performance with (i) increasing presence of distributed energy resources (DER) at the low-voltage level, and (ii) variable wind power generation at the high-voltage level. The paper investigates various system configurations with increasing presence of microgrids, with active devices. System simulations quantify system performance in terms of cost, first using the traditional single-level optimization framework, and second using the proposed bi-level framework. Comparisons between the system with traditional, passive distribution systems and with microgrids are also presented, with results again quantified via the interconnected system operating costs. Results show that at low levels of DER and microgrid penetration, traditional (single-level) system optimization algorithms perform adequately as compared to the proposed bi-level optimization framework. However, as DER and microgrid penetration increase, the traditional single-level framework does not accurately capture the full system benefits of distributed technologies. The results demonstrate that new optimization algorithms, such as the proposed bi-level framework, will be required if the benefits of DER are to be accurately quantified in the evolving power system

Approximate Chance-Constrained Unit Commitment Under Wind Energy Penetration

We study a multi-period unit commitment problem under wind energy penetration, in which the load balance is enforced with a predefined confidence level across the whole system and over the planning horizon. Since, except for special cases, chance-constrained problems are non-convex, we analyze two relaxations of the load balance based upon robust optimization ideas and estimated quantiles of the marginal distributions of the net load processes. The approximation proposals are benchmarked against the well-known scenario approximation. Under the scenario approach, we also analyze a simple decomposition strategy to find a lower bound of the approximate problem, when the latter becomes intractable due the size of the set of scenarios. The reliability of the obtained solutions as well as their runtimes are examined on three widespread test systems

Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior

<p>We explore the behavior of wind speed over time, using a subset of the Eastern Wind Dataset published by the National Renewable Energy Laboratory. This dataset gives modeled wind speeds over three years at hundreds of potential wind farm sites. Wind speed analysis is necessary to the integration of wind energy into the power grid; short-term variability in wind speed affects decisions about usage of other power sources, so that the shape of the wind speed time series becomes as important as the overall level. To assess differences in intra-day time series, we propose a functional distance measure, the band distance, which extends the band depth of López-Pintado and Romo. This measure emphasizes the shape of time series or functional observations relative to other members of a dataset and allows clustering of observations without reliance on pointwise Euclidean distance. We show a method for adjusting for seasonal effects in wind speed, and use these standardizations as input for the band distance. We demonstrate the utility of the new method in simulation studies and an application to the MOST power grid algorithm, where the band distance improves reliability over standard methods at a comparable cost.</p