Optimal management of a hybrid and isolated microgrid in a random setting

Nowadays, governments and electricity companies are making efforts to increase the integration of renewable energy sources into grids and microgrids, thus reducing the carbon footprint and increasing social welfare. Therefore, one of the purposes of the microgrid is to distribute and exploit more zero emission sources. In this work, a Stochastic Unit Commitment of a hybrid and isolated microgrid is developed. The microgrid supplies power to satisfy the demand response by managing a photovoltaic plant, a wind turbine, a microturbine, a diesel generator and a battery storage system. The optimization problem aims to reduce the operating cost of the microgrid and is divided into three stages. In the first stage, the uncertainties of the wind and photovoltaic powers are modeled through Markov processes, and the demand power is predicted using an ARMA model. In the second stage, the stochastic unit commitment is solved by considering the system constraints, the renewable power production, and the predicted demand. In the last stage, the real-time operation of the microgrid is modeled, and the error in the demand forecast is calculated. At this point, the second optimization problem is solved to decide which generators must supply the demand variation to minimize the total cost. The results indicate that the stochastic models accurately simulate the production of renewable energy, which strongly influences the total cost paid by the microgrid. Wind production has a daily impact on total cost, whereas photovoltaic production has a smoother impact, shown in terms of general trend. A comparison study is also considered to emphasize the importance of correctly modeling the uncertainties of renewable power production in this context

Destabilizing attack and robust defense for inverter-based microgrids by adversarial deep reinforcement learning

The droop controllers of inverter-based resources (IBRs) can be adjustable by grid operators to facilitate regulation services. Considering the increasing integration of IBRs at power distribution level systems like microgrids, cyber security is becoming a major concern. This paper investigates the data-driven destabilizing attack and robust defense strategy based on adversarial deep reinforcement learning for inverter-based microgrids. Firstly, the full-order high-fidelity model and reduced-order small-signal model of typical inverter-based microgrids are recapitulated. Then the destabilizing attack on the droop control gains is analyzed, which reveals its impact on system small-signal stability. Finally, the attack and defense problems are formulated as Markov decision process (MDP) and adversarial MDP (AMDP). The problems are solved by twin delayed deep deterministic policy gradient (TD3) algorithm to find the least effort attack path of the system and obtain the corresponding robust defense strategy. The simulation studies are conducted in an inverter-based microgrid system with 4 IBRs and IEEE 123-bus system with 10 IBRs to evaluate the proposed method

Smart Grid for the Smart City

Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning

With the development of microgrids (MGs), an energy management system (EMS) is required to ensure the stable and economically efficient operation of the MG system. In this paper, an intelligent EMS is proposed by exploiting the deep reinforcement learning (DRL) technique. DRL is employed as the effective method for handling the computation hardness of optimal scheduling of the charge/discharge of battery energy storage in the MG EMS. Since the optimal decision for charge/discharge of the battery depends on its state of charge given from the consecutive time steps, it demands a full-time horizon scheduling to obtain the optimum solution. This, however, increases the time complexity of the EMS and turns it into an NP-hard problem. By considering the energy storage system’s charging/discharging power as the control variable, the DRL agent is trained to investigate the best energy storage control method for both deterministic and stochastic weather scenarios. The efficiency of the strategy suggested in this study in minimizing the cost of purchasing energy is also shown from a quantitative perspective through programming verification and comparison with the results of mixed integer programming and the heuristic genetic algorithm (GA)

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces