A rapid review on community connected microgrids

As the population of urban areas continues to grow, and construction of multi-unit developments surges in response, building energy use demand has increased accordingly and solutions are needed to offset electricity used from the grid. Renewable energy systems in the form of microgrids, and grid-connected solar PV-storage are considered primary solutions for powering residential developments. The primary objectives for commissioning such systems include significant electricity cost reductions and carbon emissions abatement. Despite the proliferation of renewables, the uptake of solar and battery storage systems in communities and multi-residential buildings are less researched in the literature, and many uncertainties remain in terms of providing an optimal solution. This literature review uses the rapid review technique, an industry and societal issue-based version of the systematic literature review, to identify the case for microgrids for multi-residential buildings and communities. The study describes the rapid review methodology in detail and discusses and examines the configurations and methodologies for microgrids

Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.Comment: 22 pages, 8 figures, to be submitted to Applied Energy of Elsevie

Robust optimization for energy transactions in multi-microgrids under uncertainty

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles

Architectures for smart end-user services in the power grid

Abstract-The increase of distributed renewable electricity generators, such as solar cells and wind turbines, requires a new energy management system. These distributed generators introduce bidirectional energy flows in the low-voltage power grid, requiring novel coordination mechanisms to balance local supply and demand. Closed solutions exist for energy management on the level of individual homes. However, no service architectures have been defined that allow the growing number of end-users to interact with the other power consumers and generators and to get involved in more rational energy consumption patterns using intuitive applications. We therefore present a common service architecture that allows houses with renewable energy generation and smart energy devices to plug into a distributed energy management system, integrated with the public power grid. Next to the technical details, we focus on the usability aspects of the end-user applications in order to contribute to high service adoption and optimal user involvement. The presented architecture facilitates end-users to reduce net energy consumption, enables power grid providers to better balance supply and demand, and allows new actors to join with new services. We present a novel simulator that allows to evaluate both the power grid and data communication aspects, and illustrate a 22% reduction of the peak load by deploying a central coordinator inside the home gateway of an end-user

Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios

This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from a photovoltaic source. In the algorithm for optimal scheduling, each load is modeled with a dynamic power profile that may be different for on and off switching. Optimal scheduling is achieved by the evaluation of a user-specified criterion function with possible power constraints. The scheduling algorithm exploits the use of a moving finite time horizon and the resulting finite number of scheduling combinations to achieve real-time computation of the optimal timing and switching of loads. The moving time horizon in the proposed optimal scheduling algorithm provides an opportunity to use short term (time moving) predictions of solar power based on advection of clouds detected in sky images. Advection, persistence, and perfect forecast scenarios are used as input to the load scheduling algorithm to elucidate the effect of forecast errors on mis-scheduling. The advection forecast creates less events where the load demand is greater than the available solar energy, as compared to persistence. Increasing the decision horizon leads to increasing error and decreased efficiency of the system, measured as the amount of power consumed by the aggregate loads normalized by total solar power. For a standalone system with a real forecast, energy reserves are necessary to provide the excess energy required by mis-scheduled loads. A method for battery sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201

Forecast-based Energy Management Systems

The high integration of distributed energy resources into the domestic level has led to an increase in the number of consumers becoming prosumers (producer + customer), which creates several challenges for network operators, such as controlling renewable energy sources over-generation. Recently, self-consumption as a new approach is encouraged by several countries to reduce the dependency on the national grid. This work presents two different Energy Management System (EMS) algorithms for a domestic Photovoltaic (PV) system: (a) real-time Fuzzy Logic-based EMS (FL-EMS) and (b) day-ahead Mixed Integer Linear Programming-based EMS (MILP-EMS). Both methods are tested using the data from the Active Office Building (AOB) located in Swansea University, Bay Campus, UK, as a case study to demonstrate the developed EMSs. AOB comprises a PV system and a Li-ion Battery Storage System (BSS) connected to the grid. The MILP-EMS is used to develop a Community Energy Management System (CEMS) to facilitate local energy exchange. CEMS is tested using the data from six houses located in London, UK, to form a community. Each household comprises a PV system and BSS connected to the grid. It is assumed that all six households use an EV and are equipped with a bidirectional charger to facilitate the Vehicle to House (V2H) mode. In addition, two shiftable appliances are considered to shift the demand to the times when PV generation is maximum to maximise community local consumption. MATLAB software is used to code the proposed systems. The FL-EMS exploits day-ahead energy forecast (assumed it is available from a third party) to control the BSS with the aim of reducing the net energy exchange with the grid by enhancing PV self-consumption. The FL-EMS determines the optimal settings for the BSS, taking into consideration the BSS's state of health to maximise its lifetime. The results are compared with recently published works to demonstrate the effectiveness of the proposed method. The proposed FL-EMS saves 18% on total energy costs in six months compared to a similar system that utilises a day-ahead energy forecast. In addition, the method shows a considerable reduction in the net energy exchanged between the AOB and the grid. The main objective of the MILP-EMS is to reduce the net energy exchange with the grid by including a two days-ahead energy forecast in the optimisation process. The proposed method reduces the total operating costs (energy cost + BSS degradation cost) by up to 35% over six months and reduces net energy exchanged with the grid compared to similar energy optimisation technique. The proposed cost function in MILP-EMS shows that it can outperform the performance of alternative cost function that directly reduce the net energy exchange. CEMS uses two days-ahead energy forecast to reduce the net energy exchange with the grid by coordinating the distributed BSSs. The proposed CEMS reduces the total operating costs (energy costs + BSSs degradation costs) of the community by 7.6% when compared to the six houses being operated individually. In addition, the proposed CEMS enhances community self-consumption by reducing the net energy exchange with the grid by 25.3% over four months compared to similar community energy optimisation technique. A further reduction in operating costs is achieved using V2H mode and including shiftable appliances. Results show that introducing the V2H mode reduces both the total operating costs of the community and the net energy exchange with the grid