Planning Flexibility With Non-Deferrable Loads Considering Distribution Grid Limitations

Abstract

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

Optimal configuration of energy storage considering flexibility requirements and operational risks in a power system

“…Moreover, a framework for flexibility recovery guarantees for the ESS has been proposed, aiming to ensure full cycle capacity and accurate loss accounting (Evans et al, 2022). Notably, existing literature is predominantly centered on the planning and optimal operation of the ESS within distribution networks (Alharbi and Almutairi, 2021) and microgrids (Shen et al, 2021), warranting further research into the configuration strategy of the ESS within the transmission system to bolster the flexibility resource reserve of the power system. The contributions of this paper can be summarized below.…”

Integrating Internet-of-Things-Based Houses into Demand Response Programs in Smart Grid

“…The IEEE Reliability Test System [37] is used to generate the system load profiles. The house peak load is assumed to be 2.08 kW [38] to calculate the number of houses at each bus, and it is also assumed that all loads are residential loads. The percentage of flexible loads that can be deferred via direct DR programs is assumed to be 10%.…”

Assessment of Distribution System Margins Considering Battery Swapping Stations

“…The distribution of trip distances, the time-of-day distribution of the trips, and the number of trips associated with each vehicle are extracted from [43], to be used for predicting the arrival times for EVs at the BSS. A developed method in [44], is adopted in this work to track trip information for each vehicle and determine the arrival time for each EV at the BSS. The SOC of an EV is checked considering its distancedriven mileage for each trip, and when the vehicle depletes the entire SOC window, either the begin time or the finish time of that trip is recorded For instance, the begin time of that trip will be recorded for a battery swapping when the EV depletes its SOC before finishing the trip.…”

“…To describe the overall process of charging multiple EVs served at an EV station, queuing theory is thereafter employed. The EV loads of charging station are estimated using the method developed in [44]. For a detailed explanation and discussion of modeling EV loads using a battery charging service, the reader may refer to [44].…”