Optimal stochastic scheduling in microgrids under managed risk

Nowadays, the use of electrical and thermal storage in smart microgrids has become popular. On the other hand, the Demand Response Programs (DRPs) can play an important role in management of the smart microgrids. In this paper, a new practical model is proposed to evaluate the effectiveness of the electrical and thermal storage systems as the two most important parts of the future smart microgrids. Furthermore, the DRPs have been used as a potential to reduce the operation cost of the smart microgrid. The proposed Mixed Integer Nonlinear Programming (MINLP) model is developed and implemented using GAMS software and it is solved with BARON solver. Different scenarios have been simulated and the results are shown. The results show that the operation cost of the microgrid in presence of DRPs and electrical storage is significantly reduced.

As the proportion of renewable energy power generation continues to increase, the number of grid-connected microgrids is gradually increasing, and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community (MGC). In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community, this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid. The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid. The upper-level model optimizes the goal of maximizing the social welfare of the microgrid. Taking a microgrid community with four microgrids as an example, the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid, improve the overall revenue of the microgrid community, and reduce the power interaction pressure on the main grid.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

The growth of smart grids has led to number of challenges in order to maintain power quality and reliability. On the other hand, the advanced technologies are helping to address the issues that arise due to the heterogeneous entities such as Intermittent Renewable Energy Sources (IRES) and Electric Vehicles (EV's). EV's storage can be utilized to support the energy management in micro grids and other flexible areas like Industries and educational organizations. In order to use EV s as virtual storage units, one has to ensure all the constraints and limitations that are involved. Above all, EV usage for grid energy management should not put EV owner in a chaotic state. There has been numerous EV control strategies proposed for V2G and G2V operations since last decade. Still, it is a big challenge in the real-time environment to address sensitive issues such as: owner flexibility, battery degradation, economic benefit and other uncertainties. This work mainly focuses on maximization of EV storage usage with consideration of battery degradation. A prioritization-based EV strategy is proposed using Adaptive Neuro-Fuzzy Inference System (ANFIS) with four decision variables. A win-win strategy for maximization of battery lifetime and EV exploitation is considered during the prioritization. The proposed EV technique is implemented for educational organization with real-time travel data. The case study presented in this article provides the comprehensive analysis on the impact of proposed control strategy in different aspects.

The growth of smart grids has led to the number of challenges to maintain the power quality and reliability. On the other hand, the advanced technologies are helping to address the issues that arise due to the heterogeneous entities such as Intermittent Renewable Energy Resources (IRER) and Electric Vehicles (EV). The EV’s storage can be utilized to support the energy management in the micro grids and other flexible areas like industries and educational organizations. In order to use EVs as virtual storage units, one has to ensure all the constraints and limitations that are involved. Above all, EV usage for grid energy management should not put EV owner in a chaotic state. There has been numerous EV control strategies proposed for Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations since last decade. This work mainly focuses on maximization of EV storage usage with consideration of battery degradation. A prioritization based EV strategy is proposed using Adaptive Neuro-Fuzzy Inference System (ANFIS) with four decision constraints.KeywordsEV battery degradationEVEV prioritizationGrid dependencyG2VOptimal energy distributionV2G

Multi types DG expansion dynamic planning in distribution system under stochastic conditions using Covariance Matrix Adaptation Evolutionary Strategy and Monte-Carlo simulation

During the past few years, microgrids (MGs) have been becoming more attractive as effective means to integrate different distributed energy resources (DERs). To coordinate active and reactive power sharing among DERs, conventional droop control method is widely used as a decentralized control scheme. However, sharing powers among the sources based on the units' rated capacities is not an optimal solution in terms of economy and efficiency. In this paper, a new adaptive droop-based control strategy is proposed for AC MGs to optimally share MG load between corresponding units. The mentioned control strategy is developed in two levels. The upper control level is a mixed-objective optimization algorithm that provides optimal set-points for power generations considering system's constraints and goals, while the lower control level is responsible for tracking the reference signals coming from the upper level. To demonstrate the effectiveness of the proposed control strategy under different operating scenarios, simulation results in a benchmark MG are also presented.

Impact of energy storage system and distributed energy resources on bidding strategy of micro-grid in deregulated environment

Real-time pricing for smart grid with multi-energy microgrids and uncertain loads: a bilevel programming method

Renewable Energies play crucial role in modern distribution systems. An active distribution system consists of several Micro-Grids (MGs) each of which has kinds of Distributed Generations (DGs) and Demand Response (DR) resources. Hence MG optimal operation is bi-level optimization problem whose upper level seeks for micro-grid owner profit and the lower level corresponds to distribution companies (DC) profit. In such a system MGs can exchange power between each other as well as distribution company. Therefore, upper level problem determines DGs generation, curtailed load, power exchange with other MGs subject to load balance constraint. Also Distribution Company (DISCO) clears retail market. On the other hand, renewable energies uncertainty and variably makes the MGs operation more complicated. In this paper, renewable energy variability handled by shift able load management and their uncertainty has been managed by curtailable load. The developed model has been solved by Particle Swarm Optimization (PSO) for upper level and then with given population in each iteration, lower level problem has been solved completely by other PSO package. This process ends when upper level PSO converge. Numerical studies have been shown that renewable management by proposed model leads to more optimal solutions.

Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach

For optimal microgrid (MG) operation, one challenge is the supply of cooling and electricity energy is a coupled co-optimization issue when considering the combined cooling, heating and power (CCHP) units and ice-storage air-conditioners. Another challenge is the inherent randomness of renewable energy within the MG should be accommodated by MG itself. In Part I of this two-part paper, the partial load performance of CCHPs and the performance of ice-storage air-conditioners are modeled, and the cooling and electricity coordinated MG day-ahead scheduling and real-time dispatching models are established. In day-ahead scheduling model, the uncertainty of wind and solar power is represented by multi-scenarios and the objective is to achieve the minimal expected MG operation cost. In real-time dispatching model, the different time-scale dispatch schemes are respectively applied for cooling and electricity to smooth out the fluctuations of renewable energy supply and to follow the variations of cooling and electricity demands by the fine dispatching of the components within MG such that the impact of MG to the connected main grid is minimal. The proposed MG multi time-scale cooling and electricity coordinated schedule achieves an integrated optimization for multi energy-type supply, and makes the MG be controllable as seen from the main grid.

Microgrid dynamic combined power–heat economic-emission dispatch with deferrable loads and price-based energy storage elements and power exchange

Traditionally, the economic operation of a microgrid (MG) is independent with that of a distribution network (DN), and the electricity trade between them is simply based on contract prices or wholesale electricity market prices. In order to coordinate the benefits of DNs and MGs more effectively, a new economic operation model for DNs with MG is proposed in this paper modeled as a bilevel programming problem (BLPP). At the upper level, electric power from the gird and electricity price of MGs are control variables. A DN seeks the least operation cost by optimizing these control variables. At the lower level, control variables are electric power of dispatchable distributed generations (DGs) and the DN. Based on the electricity price got from the upper level and the operation costs of dispatchable DGs, a MG minimizes its hourly operation cost by optimizing the control variables. Furthermore, electricity price constraints are considered in this BLPP model, and a method to dynamically determine the constraints is proposed. They are decided by the load power demand profile in the DN and the price differential between the wholesale electricity market and dispatchable DGs. A comprehensive real-time pricing for residential consumers is also considered in the operation model to study the impact from demand response. The proposed BLPP model is solved by particles swarm optimization based on bilevel iterative algorithm. The demonstration is performed on a modified IEEE 33-node radial distribution system.

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi‐objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.