Optimal operation of a 30kW natural gas microturbine cluster

This paper discusses optimal operation strategies of a 30 kW natural gas microturbine (MT) cluster for electrical load-following applications. The cluster operation assures a good operational flexibility, but, at the same time, also the aspect of the partial-load MT characteristics, in terms of energy efficiency and pollutant emissions has to be taken into consideration. In particular, the experimental results show that the NOx and CO emissions are higher when the MT is operated below its rated capacity. Under these circumstances, the Evolutionary Algorithm has been employed in order to optimize the operation of this cluster from the point of view of the pollutant emissions and fuel consumption

Voltage Control in Low-Voltage Grids Using Distributed Photovoltaic Converters and Centralized Devices

This paper studies the application of distributed and centralized solutions for voltage control in low voltage (LV) grids with high photovoltaic (PV) penetration. In traditional LV grids, the coordination of distributed PV converters and a centralized device would require massive investments in new communication and control infrastructures. The alternative of exploiting distributed PV converters for voltage control is discussed, showing that it can help to stabilize the voltage in the grid connection points also without coordination between them and/or with a centralized unit. The goal of this paper is to investigate how the setup of the voltage controllers inside PV inverters affects the operation of these controllers taking into account the limits for reactive power injection. In addition, the interaction of distributed PV converters with centralized devices (static var compensators and on load tap changers) is analyzed to assess whether additional benefits may come in these cases

Optimal operation of a microturbine cluster with partial-load efficiency and emission characterization

This paper discusses optimal operation strategies of a cluster of microturbines (MTs) for electrical load-following applications. Cluster operation ensures higher operational flexibility, but raises the issue of taking into account the partial- load MT characteristics, in terms of energy efficiency and pollutant emissions. In particular, from experimental results the NOx and CO emissions exhibit nonlinear and to some extent complementary trends at different partial-load levels. Hence, individual optimizations of fuel consumption and emission reduction are first carried out in this paper to show the conflicting nature of such objectives. Then, multi-objective optimization is performed to directly determine the best-known Pareto front. For this purpose, a procedure based on evolutionary programming is illustrated and applied to a practical case study. The results point out the degree of trade-off that can be sought when minimizing the local environmental impact of such distributed energy systems

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data

PV system integration in buildings: An energy and economic case study

PV systems, due to their economies of scale, are nowadays increasingly adopted. The direct consequence of these economies is the cost reduction of the commercial PV technologies (in particular, the crystalline silicon one). Thus, the goal of this paper is to analyze and assess the energy and economic benefits of a PV system integration onto a public library building. The simulations of PV and load power profiles are carried out in this study for different configurations of the PV generator with the goal of minimizing the worsening effect of shading caused by obstacles near the library building