An Optimized Framework for Energy Management of Multi-Microgrid Systems

Regarding different challenges, such as integration of green energy and autonomy of microgrid (MG) in the multi-microgrid (MMG) system, this paper presents an optimized and coordinated strategy for energy management of MMG systems that consider multiple scenarios of MGs. The proposed strategy operates at two optimization levels: local and global. At an MG level, each energy management system satisfies its local demand by utilizing all available resources via local optimization, and only sends surplus/deficit energy data signals to MMG level, which enhances customer privacy. Thereafter, at an MMG level, a central energy management system performs global optimization and selects optimized options from the available resources, which include charging/discharging energy to/from the community battery energy storage system, selling/buying power to/from other MGs, and trading with the grid. Two types of loads are considered in this model: sensitive and non-sensitive. The algorithm tries to make the system reliable by avoiding utmost load curtailment and prefers to shed non-sensitive loads over sensitive loads in the case of load shedding. To verify the robustness of the proposed scheme, several test cases are generated by Monte Carlo Simulations and simulated on the IEEE 33-bus distribution system. The results show the effectiveness of the proposed model.

Probabilistic Optimization of Networked Multi-Carrier Microgrids to Enhance Resilience Leveraging Demand Response Programs

Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are addressed. The DRPs unlock the flexibility potentials of domestic demands to compensate for the power shortage during critical hours. The heat-power dual dependency characteristic of combined heat and power systems as a substantial technology in microgrids is considered in the model. The simulation results confirm that the suggested NMCMG not only integrates the flexibility potentials into the microgrids but also enhances the resilience of the energy systems.

A Stochastic Multi-Criteria Decision Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of Grid-interactive Efficient Buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

Load Shedding in Microgrids with Consideration of Voltage Quality Improvement

Microgrids have become more and more popular their usefulness as a renewable energy resource has been recognized. The core ability and promise of microgrids is addressing the environmental concerns due to climate change that have been growing during recent years. The innovation of microgrids is that they are designed to operate either in island mode or interconnected with the main grid system. However, when the microgrid operates in islanded mode, faults may occur which can cause system collapse or even blackout. Load curtailment schemes can be utilized to decrease the quantity of associated load to a level that can be securely supported by accessible generation in isolated mode. The main goal of this research is to evaluate the optimal amount of shedding power considering sustainable power sources, with the help of primary and secondary adjustments of the generator to restore the frequency to the allowed range. Particle Swarm Optimization algorithm is applied in this paper to determine the distributed shedding power on each demand load bus which can improve the voltage quality of the isolated microgrid system. The effectiveness of the proposed method is demonstrated through the simulation of IEEE 16- bus microgrid.

RES and ES Integration in Combination with Distribution Grid Development Using MILP

In the paper, a new method of long-term planning of operation and development of the distribution system, taking into account operational aspects such as power flows, power losses, voltage levels, and energy balances, is presented. The developed method allows for the allocation and selection of the power of Renewable Energy Sources (RES), control of energy storage (ES), curtailing of RES production (EC), and the development of the distribution grid (GD). Different types of RES and loads are considered, represented by generation/demand profiles reflecting their typical operating conditions. RES allocation indicates the node in the distribution system and the power level for each type of RES that may be built. Energy storage (ES) allows generation to be transferred from the demand valley to the peak load. Curtailment of RES generation indicates the moment and level of power by which generation will be reduced, while the grid development (GD) determines between which network nodes a new power line should be built. All these activities allow to minimize the costs of planning work and development of the distribution system at a specific level of energy consumption from RES in the analyzed distribution system using a Mixed Integer-Linear Programming (MILP).

An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems

With the growing robustness of modern power systems, the occurrence of load curtailment events is becoming lower. Hence, the simulation of these events constitutes a challenge in adequacy indices assessment. Due to the rarity of the load curtailment events, the standard Monte Carlo simulation (MCS) estimator of adequacy indices is not practical. Therefore, a framework based on the enhanced cross-entropy-based importance sampling (ECE-IS) method is introduced in this paper for computing the adequacy indices. The framework comprises two stages. Using the proposed ECE-IS method, the first stage’s purpose is to identify the samples or states of the nodal generation and load that are greatly significant to the adequacy indices estimators. In the second stage, the density of the input variables’ conditional on the load curtailment domain obtained by the first stage are used to compute the nodal and system adequacy indices. The performance of the ECE-IS method is verified through a comparison with the standard MCS method and the recent techniques of rare events simulation in literature. The results confirm that the proposed method develops an accurate estimation for the nodal and system adequacy indices (loss of load probability (LOLP), expected power not supplied (EPNS)) with appropriate convergence value and low computation time.