Search

The widespread installation of 5G base stations has caused a notable surge in energy consumption, and a situation that conflicts with the aim of attaining carbon neutrality. Numerous studies have affirmed that the incorporation of distributed photovoltaic (PV) and energy storage systems (ESS) is an effective measure to reduce energy consumption from the utility grid. The optimization of PV and ESS setup according to local conditions has a direct impact on the economic and ecological benefits of the base station power system. An improved base station power system model is proposed in this paper, which takes into consideration the behavior of converters. And through this, a multi-faceted assessment criterion that considers both economic and ecological factors is established. Then, the PV and ESS capacity optimization for base stations under multiple scenarios is realized. The case study indicates that the optimization process of PV and ESS is significantly influenced by the behavior of the converter. [ABSTRACT FROM AUTHOR]

Electric microgrids based mainly on renewable energies have seen a big expansion in recent years due to the great advantages they present against fossil fuels. Nowadays different governments are becoming aware of the use of environmentally friendly energies, so progressive investment has been granted to the consumers in Spain. Many microgrids have been installed in both connected and isolated circumstances, and this fact brings new challenges regarding the technical and economic points of view. This study aims to delve into electrical microgrids based on renewable energies applied in industrial facilities through a review of the literature associated with the study of general microgrids. It later focuses on the relationships between renewable energies and industrial environments. A brief reference is made to the use of energy storage systems in industrial installations depicting the most used systems. This paper presents a review of the state of the art of microgrids from distributed energy resources technologies to industrial microgrids optimization, with the primary objective of providing insight on current trends and directions in research and further identifying areas in need of further development. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the two‐stage hierarchical control of microgrids (MG) that can operate in both grid‐connected and off‐grid modes. The procedure consists in the segmentation of the control scheme in the primary and secondary levels, thus enabling the application of faster and slower dynamics according to the process. A decentralized droop control‐based method is used in the primary level, while a distributed multi‐agent coordination scheme is applied in the secondary one. The multi‐agent system is run on Python Agent DEvelopment (PADE) environment, a FIPA‐compliant agent framework. The key contributions this paper provides are the control strategies of reactive power output of non‐dispatchable sources, dynamic sharing of energy storage systems, and the management of loads of microgrids, all of them based on multi‐agent system. Co‐simulations using PSCADTM and PADE are performed considering several operating scenarios of the MG resources under different load conditions. Simulation results validate the accurate performance of the proposed control strategies. [ABSTRACT FROM AUTHOR]

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model. [ABSTRACT FROM AUTHOR]

Multiple wave energy converter (WEC) buoys can be used to establish a WEC array-powered microgrid collectively forming a Marine Energy Grid (MEG). An oceanic domain with gravity waves will have significant spatial variability in phase, causing the power produced by a WEC array to have high peak-to-average ratios. Minimizing these power fluctuations reduces the demand for large energy storage by WEC array-powered DC microgrids while also reducing losses in the undersea cable to the shore. Designs that reduce energy storage requirements are desirable to reduce deployment and maintenance costs. This work demonstrates that polyphase power in conjunction with an energy storage system can be used to maintain constant power. This work shows that an N WEC array geometry can be designed to reduce the energy storage requirements needed to mitigate the power fluctuations if the WEC array produces constant, polyphase power. Additionally, the conditions that identify the wave frequencies and control the effort needed to produce polyphase power are developed. This paper also shows that increasing the number of WECs in an array reduces aggregate power fluctuations. Finally, WEC array power profiles are investigated using simulation results to verify the mathematical conditions developed for the three and six WEC cases. [ABSTRACT FROM AUTHOR]

Past studies of microgrids have been based on measurements of fuel consumption by generators under static loads. There is little information on the fuel efficiency of generators under time-varying loads. To help analyze the impact of time-varying loads on optimal generator operation and fuel consumption, we formulate a mixed-integer linear optimization model to plan generator and energy storage system (ESS) operation to satisfy known demands. Our model includes fuel consumption penalty terms on time-varying loads. We exercise the model on various scenarios and compare the resulting optimal fuel consumption and generator operation profiles. Our results show that the change in fuel efficiency between scenarios with the integration of ESS is minimal regardless of the imposed penalty placed on the generator. However, without the assistance of the ESS, the fuel consumption increases dramatically with the penalty imposed on the generator. The integration of an ESS improves fuel consumption because the ESS allows the generator to minimize power output fluctuation. While the presence of a penalty term has a clear impact on generator operation and fuel consumption, the exact type and weight of the penalty appears insignificant; this may provide useful insight for future studies in developing a real-time controller. [ABSTRACT FROM AUTHOR]

• A multi-objective renewable energy management scheme is proposed. • Cost saving, green consumption and power quality are the main motivations. • A novel multi-agent system with three electricity use mode is developed. • Key implications and recommendations for policy makers are given. This paper proposes a multi-objective renewable energy management scheme to satisfy the needs of diversified community power consumers, wherein the multi-agent system is employed for coordinating and controlling the power generation and consumption units. Firstly, a multi-agent based microgrid and home energy management system is constructed. Subsequently, system models for renewable energy sources, electrical vehicles, residential loads and the cost function are constructed to form the microgrid operation constraints. With these constraints, three optimization models are proposed to minimize the electricity bills, the power purchased from the main grid, and optimize the power quality. The three objectives are proposed to reflect the power consumer's needs for cost saving, green consumption, and power reliability, respectively. Meanwhile, a coordination strategy which balances the three objectives is proposed. After that, a novel multi agent system is proposed to realize the proposed objectives. Four case studies and a sensitivity analysis are conducted to verify the effectiveness of the proposed method. The case study results show that: in electricity minimization, 2–6.5% of total electricity bills can be saved; in main grid power consumption minimization, the peaks of the power consumption profiles are shaved while the valleys of the profiles are filled; in power quality enhancement, the steady state frequency drop is reduced for 0.35 Hz to 0.38 Hz. The sensitivity analysis shows that communities equipped with energy storage systems accounting for 30% of the total load can achieve best optimization outcomes; the increase in renewable energy usage will lead to higher electricity bills and poorer power quality if the renewable energy sources are limited, while the availability of more renewable energy sources will compensate for the negative effects of using renewable energy sources. [ABSTRACT FROM AUTHOR]

There is a surge in the total energy demand of the world due to the increase in the world's population and the ever-increasing human dependence on technology. Conventional non-renewable energy sources still contribute a larger amount to the total energy production. Due to their greenhouse gas emissions and environmental pollution, the substitution of these sources with renewable energy sources (RES) is desired. However, RES, such as wind energy, are uncertain, intermittent, and unpredictable. Hence, there is a need to optimize their usage when they are available. This can be carried out through a flexible operation of a microgrid system with the power grid to gradually reduce the contribution of the conventional sources in the power system using energy storage systems (ESS). To integrate the RES in a cost-effective approach, the ESS must be optimally sized and operated within its safe limitations. This study, therefore, presents a flexible method for the optimal sizing and operation of battery ESS (BESS) in a wind-penetrated microgrid system using the butterfly optimization (BO) algorithm. The BO algorithm was utilized for its simple and fast implementation and for its ability to obtain global optimization parameters. In the formulation of the optimization problem, the study considers the depth of discharge and life-cycle of the BESS. Simulation results for three different scenarios were studied, analyzed, and compared. The resulting optimized BESS connected scenario yielded the most cost-effective strategy among all scenarios considered. [ABSTRACT FROM AUTHOR]