Showing papers by "Yahia Baghzouz published in 2022"
DOI•
TL;DR: In this paper , a deep reinforcement learning (DRL)-based Volt-VAR co-optimization technique was proposed for reducing voltage fluctuations as well as power loss under high penetration of DERs.
Abstract: Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt-VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.
1 citations
26 Oct 2022
TL;DR: In this article , the performance of some solar forecasting tools that are available publicly over a 24-hour time horizon is evaluated in terms of the forecasted direct normal irradiance and global horizontal irradiance (GHI) which were compared to the measurements made locally.
Abstract: This paper evaluates the performance of some solar forecasting tools that are available publicly over a 24-hour time horizon. These tools include one open-source, namely PVlib-python, and two commercial ones, each with different time resolutions. These tools are evaluated in terms of the forecasted direct normal irradiance (DNI) and global horizontal irradiance (GHI) which were compared to the measurements made locally, under both clear-sky, partly cloudy-sky and cloudy-sky conditions. Standardized metrics, including mean bias error, mean absolute error, mean absolute percent error, and root mean square error are used for comparison purposes. The goal is to determine how accurate these tools are for day-ahead solar forecasting, without resorting to sky imaging or ground-based irradiance monitoring.
29 May 2022
TL;DR: In this article , the authors proposed an energy storage as a service (ESaaS) approach to reduce the electricity bill of a residential customer with a PV system and a controllable AC load using time series smart meter load data, and local Time-Of-Use (TOU) utility rates.
Abstract: A significant portion of the annual residential electricity bill in areas that offer variable rates is associated with the summer on-peak period. Customers can reduce their bills by controlling flexible loads, and those with PV systems can achieve extra savings by installing a battery energy storage (BES) system, but this latter is often not economical due the high capital cost. In this article, a lease an energy storage block from a front-of-the-meter utility-owned storage system - known as Energy Storage as a Service (ESaaS) is proposed instead. Potential cost savings from air conditioning (AC) load control in addition to leasing an ESaaS block are evaluated for different block sizes and leasing costs using a heuristic optimization method. The approach is illustrated by analyzing the electricity bill of a residential customer with a PV system and a controllable AC load, using time series smart meter load data, and local Time-Of-Use (TOU) utility rates.