Design, Control, and Operation of Microgrids in Smart Grids

This chapter presents novel structured single-input interval type-2 fuzzy logic controllers (SI-IT2-FLCs) for the frequency damping of multi-microgrids (MMGs), whereas the application of electric vehicles (EVs) is considered in this context. For this purpose, a new SI-IT2-fuzzy PD/fuzzy PI (SI-IT2-FPD/FPI) controller is designed on two levels. Initially, an improved whale optimization algorithm, called IWOA, is adopted to adjust the setting of the gains embedded in the FPD/FPI section effectively. Then, the impact of the footprint of uncertainty (FOU), to offer extra design freedom, on control surface generation of SI-IT2-FLC has been investigated. In this way, various control surfaces were generated by varying a single coefficient which forms the FOU. Lastly, by adopting hardware-in-the-loop (HIL) simulator, the feasibility and usefulness of the suggested framework are verified from a real-time perspective.

This chapter proposes a discrete-time distributed mean-square consensus cooperation scheme that can achieve DC bus voltage restoration and maintain proportional current sharing of DC microgrids in mean square via a sparse communication network subject to dynamic communication topology and multiplicative noise disturbances. Since the cyber networks are exposed to multiplicative noise disturbance and the switching of dynamic communication topologies, it terribly reduces the stability and quality of whole system. To eliminate the adverse effects of dynamic communication topology and Brownian noise disturbances, a robust state-dependent multiplicative noise resiliency distributed resilient control algorithm is developed for DC microgrids. Through adopting stochastic stability theory and Lyapunov function, the sufficient conditions considering dynamic communication topology and noise interferences are derived to guarantee the stability operation of the whole closed-loop system. As a result, the suggested method decreases the sensitivity of the system to failures and increases its reliability. Finally, a DC microgrid test system in MATLAB/Simulink is utilized to verify the effectiveness of the proposed controller design scheme.

This chapter introduces a dynamic design framework of microgrid considering the future information of load growth, unit investment cost variation, and device degradation. The stochastic optimization and robust optimization techniques are utilized to deal with the long-term uncertainty of energy price and the short-term uncertainties of renewable energy-based distributed generation and load demands, respectively, among the design horizons. To speed up the design process and reduce the computational complexity, a typical scenario set generation method based on mixed integer multi-objective linear programming is applied. The microgrid design process is conducted in a dynamic perspective, which can provide not only the optimal capacities of distributed energy resources (DERs) but also the optimal investment timing for the stakeholders. Compared with the traditional microgrid design method where the DERs are installed in the first year, the proposed design framework shows better performance in terms of investment economy, renewable energy utilization rate, and fund recovery speed; therefore, it is more economically friendly to the stakeholders in the strategic planning design of microgrids.

The interconnected microgrid system (IMS) is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. To optimally coordinate the operation of microgrids owned by different owners while considering uncertainties in market environment, a bi-level distributed optimized operation method for IMS with uncertainties is proposed in this chapter. A hierarchical and distributed operational communication architecture of IMS is first established. A bi-level distributed optimization model was built for IMS, where at the upper level, the IMS operates purchase-sale mode or demand response mode with the distribution network operator and optimizes the trading power with microgrids to maximize revenue. At the lower level, the chance constraint programming is used to describe and deal with the uncertainty of renewable energy and loads and optimize the output and energy storage of distributed energy with the goal of minimum cost. The analytical target cascading and augmented Lagrange method are combined to decouple and reconstruct the bi-level model for distributed solution and establish a fair price mechanism. The optimal solutions of the problem are obtained through parallel iteration, in which the price signal plays a coordinated role in the distributed iterative optimization process. Abundant case studies verify the advantages of the model and the performance of the proposed method.

The penetration of distributed energy resources (DER) is growing worldwide, and microgrid (MG) is an approprate way to realize intergration of these DERs. The new reform of power system promotes the market-oriented operation of microgrids. This chapter takes the park microgrid with multi-stakeholder as the object, and to promote the interaction between the main grid and DERs in MG, a two-level optimization model of microgrid bidding transaction based on multi-agent system is established. In the lower-level optimization, considering the deviation penalty of power generation and the previous round bidding results, the optimal bidding strategy model is established to maximize the benefit of bidding unit agent. In the upper-level model, bidding strategies of DERs as constraints, a multiple objective mixed-integer linear programming model was built to optimize the overall objectives of clearing price and imbalanced deviation, searching for the optimal clearing price and the generation plan of DERs. Due to the complexity of the two-layer optimization model, a novel artificial immune system (AIS) was established and integrated into the multi-agent system to help DERs participate in the optimal bidding operation of MG. The antigen is transformed by the environmental information, the price of the main grid, other DERs’ bidding strategies, and the predicted deviation coefficient while considering the uncertainties of DER facilities. The proposed optimized operation mode is compared with the traditional operation mode in the case study, verifying that the proposed method can realize the optimal operation of the MG and the coordinated interaction with the main grid, increasing the benefit of stakeholders. The AIS algorithm is also compared with traditional algorithms, proving the superiority in optimizing.

Nowadays, the resilience enhancement is one of the most important concerns in electric power networks. The division of the main microgrid into several sub-microgrids, i.e., microgrid formation (MF), is a resilient strategy for distribution systems against natural disasters and cyber-physical attacks. Such effective solution not only increases the resilience and load restoration but also reduces the costs. The extensive penetration of renewable resources in microgrids increases the issues about safe operation under faults. This chapter presents a resilient microgrid formation in the presence of solar, wind, and diesel Distributed generation (DG) for load restoration maximization. In order to carry out the microgrid formation, several candidate breakers and tie-line switches are considered, and their optimal on-off conditions are determined. Both the active and reactive powers are included in the model. The model is expressed as mixed-integer linear programming (MILP) and is simulated under three various cases including case 1, without formation strategy; case 2, formation strategy with line breaker switch; and case 3, formation strategy with both line and tie breaker switches. The numerical results are carried out based on IEEE 33-bus and 69-bus standard distribution networks. The results emphasize on the effectiveness of the developed formation strategy with both the breaker and tie line switches for load restoration and resilience enhancement.