Surround control of distributed energy resources in micro-grids

A behavior-centered framework for real-time control and load-shedding using aggregated residential energy resources in distribution microgrids

Microgrids have become valuable assets because they improve the reliability of consumers while integrating renewables via distributed energy resources (DERs). Thus, making them cost-efficient is essential to secure their proliferation. This paper proposes a new method for the optimal design of microgrids. The proposed two-stage method optimizes the size and the location of the DERs, i.e., the renewable energy sources (RESs), distributed generation (DG) units, and battery energy storage systems (BESSs). Furthermore, the overall operation of the microgrid is optimized using a stochastic scenario-based approach, considering grid-connected and unintentional islanded modes. The proposed method also considers internal network reinforcements. Thus, the first stage is an energy-based approach, formulated as a mixed-integer linear programming (MILP) problem, and it is used to size the DERs, whereas the second stage uses an optimal AC power flow (AC-OPF) to formulate a mixed-integer nonlinear programming (MINLP) model that allocates the DERs and selects the best conductor for each circuit. The multi-objective nature of the problem is addressed via Pareto optimization to analyze the trade-off between operational and capital costs. The MINLP model is linearized through piece-wise approximations and solved using commercial solvers. Furthermore, the impact of battery degradation is analyzed through a simple adaptation of the Stage 1 model. Results were obtained with data from the real university campus microgrid CampusGrid, located at the State University of Campinas (UNICAMP), in São Paulo, Brazil.

In this paper, we present an event-driven automatic demand response (EADR) framework for online operation of residential microgrids (RMGs). Our framework involves comprehensive analysis of the schedulable ability (SA) for residential energy resources (RERs), and the uncertainties of both sides are also considered. Specifically, we first construct an EADR architecture to minimize the total operation cost and maintain supply–demand balance, and the event analyses are provided. Then, we define the concept of SA for RERs and also establish an SA evaluation system taking the real-time and historical information of the RERs into account. On these bases, SA-based interactions between RERs and EADR system and state machine are introduced to trigger the execution of the online EADR in a close-loop way. Furthermore, to implement this framework in practice, we functionally decompose the overall EADR architecture into event manager, EADR server, and a group of local controllers, and also design distributed online algorithms for each of them. The key idea of the designed algorithms is to operate the RMG simulating its real-life dynamics. Numerical simulations on a practical RMG verify the effectiveness of the proposed approach.

In DC microgrids, distributed energy storage plays a key role in stabilizing the DC bus voltage. The bidirectional DC/DC converter in the distributed energy storage system should be designed according to the voltage level and electromagnetic isolation requirements, and multiple energy storage units should be coordinated for load current distribution according to the state of charge (SOC). This paper proposes a SOC power index droop control strategy by communication lines to coordinate the fast and high-precision distribution of load current among multiple energy storage units, and the SOC between energy storage units quickly converges to a consistent state. Considering that communication lines are susceptible to interference, this paper further proposes an improved SOC power index droop control to overcome the effects of communication line failures. Considering the high cost of the energy storage unit, it should be connected to the DC microgrid in layers to achieve a reasonable allocation of resources in practical applications. In order to provide high-quality power to a large power grid, the quantification standards of the DC bus fluctuation range and the working range of each converter are further discussed to maximize the stability of the DC bus voltage and grid-connected power fluctuation.

Continuous and sustainable electricity is one of the major concerns in this modern world. This has led to the implementation of microgrid (MG) in order to establish an independent, efficient and cost-effective power supply system. The generation in MG can be conventional or non-conventional but due to increasing power demand, high fuel prices, scarcity of fossil fuels and degrading environment, there is a growing demand of using renewable energy sources (RS) for power generation. Solar PV units play an indispensable part in producing clean energy and coping with this modern-day power demand challenges. Grey wolf optimization (GWO), which is a metaheuristic technique inspired by the hierarchical hunting mechanism of grey wolves, is used in this chapter for solving a multi-objective problem in a dynamic environment of a microgrid. Dynamic dispatch is a more practical way which aims to provide an optimum solution in a scheduling horizon over twenty-four hours a day. A hybrid system comprising six conventional thermal plants and a solar farm containing thirteen solar PV units are discussed in this chapter. The performance and effectiveness of GWO are compared and validated with other two well-proven methods ABC and DE.

Scheduling of multi-fuel energy resources in microgrid using grey wolf optimisation

Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their <italic>dp/dt</italic> limitation, the installed energy storages cannot cover the challenge of high <italic>df/dt</italic>. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG; providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers&#x2019; values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6&#x0025; and 32&#x0025;, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum <italic>df/dt</italic> is well compatible with different standards in the presence of load perturbations and different wind speeds.

Due to emerging environmental problems from fossil fuel usage and increasing renewable energy resources in microgrids, the need of demand response (DR) is intensified to stabilize irregular balance and to mitigate carbon dioxide emissions simultaneously. However, in order to estimate practical and factual DR potential, an analysis of load characteristics including user behavior is required based on information data. Thus, in this study, a novel analytic approach is proposed to detect resident behavior for environment responsive DR potential estimation. A new framework to determine optimal DR potential is proposed with related data analysis models. In each analysis model, resident occupancy behavior and forecasted renewable energy generation are estimated. During the process of resident behavior detection, sub-metering data of appliances are analyzed and profiled for multiple purposes by a new method based on hidden Markov model and time varying Markov chain. The optimal DR potential is estimated considering guaranteed residents' comfort constraints and the dynamic characteristics of appliances. The proposed framework is tested on the single household environment of residential region and the results present that the proposed model is environmentally and economically effective, while also suggesting meaningful implications through resident behavior data analysis.

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.

Plug-in hybrid electric vehicle (PHEV) has become a significant component in the fundamental infrastructure of microgrids (MGs). It is therefore of crucial practical interest to coordinate PHEVs with the rest of the grid. Supply demand coordination is an essential way to attain high energy efficiency at the system level while satisfying the stability of the grid requirement. This problem is challenging due to the high uncertainty from both the demand and supply sides in the system. This paper proposes a PHEVs smart charging that can achieve optimal operation for MGs. The proposed power management strategy is designed to minimize the MG dependency on the main grid, and to charge PHEVs based on maximum employment of RERs. Two scenarios are considered: the first scenario is designed so that PHEVs are charged without optimization and, the second case where PHEVs is charged in a smart way with optimization in order to reduce MG dependency on the main grid. Modified IEEE 33-bus system is simulated by MATLAB to verify the performance of the proposed power management strategy. The off-line digital time-domain simulations and software verification show that energy exchange between MG and the main grid is reduced. Also, the RERs generation is increased in order to charge the PHEVs.

The increasing penetration of intermittent renewable energy resources in micro-grids poses several issues, such as stochastic power generation, demand and supply miss-match, frequency fluctuation, and economic dispatch problems. To address such critical issues, a distributed secondary control scheme based for micro-grids with varying operating cost and intermittent renewable energy resources is proposed for frequency regulation and economic load dispatch. The paper presents an adaptive distributed averaging integral control scheme with conditional uncertainties, namely varying operating costs, and renewable intermittency. The proposed control scheme adapts to the uncertainties by updating the control law parameters dynamically and can maintain overall network stability. The distributed control scheme employs communication channels for exchange of generation data from the neighboring power units for optimal power sharing and consensus among the power units. An additional controller at tertiary control layer of the hierarchical control architecture is also augmented in the control structure to economically dispatch the load and the consensus-based algorithm guarantees optimal load sharing. The proposed communication based control scheme reveals the best combination of performance and flexibility. A performance-based comparative analysis is also presented, validating the effectiveness of the proposed control scheme compared to the prior works. The robustness and performance of the proposed control scheme is illustrated through computer simulations.

This paper proposes a joint optimization model of grid-tied residential microgrid (MG) planning and operation with the support of demand side management (DSM). In this model, the residential MG is included with various distributed energy resources, such as photovoltaic units, wind turbines, microturbines, diesel engine generators (DEs), and battery energy storage systems (BESSs). The proposed model is formulated as a bi-objective optimization problem in view of economic and environmental contemplation. Therefore, the minimization of MG total annual cost and total annual emission are considered as the primary objectives. This paper mainly analyzes the benefits of DSM assistance in the process of residential MG joint planning and operation optimization. The proposed bi-objective optimization problem is converted into a single-objective optimization problem and the single-objective problem is solved using a mixed integer linear programming method. To investigate the impacts of DSM on the MG planning and operation problem, the proposed model is applied to a residential MG consisting of 1000 smart homes with different DSM participation levels. The simulation results show that the proposed model is an efficient tool for residential MG planning and operation optimization from the perspective of economic and environmental considerations.

The increasing penetration of distributed energy resources (DERs) into power systems has transformed the landscape of energy distribution, bringing both opportunities and significant challenges. This study examines the impact of DER integration on power system stability, focusing on voltage stability, frequency stability, and transient stability. A comprehensive review of 50 high-quality studies from peer-reviewed journals and reputable conference proceedings was conducted, utilizing advanced modeling, simulation, and empirical analysis methods. The findings reveal that the intermittent nature of renewable DERs, such as solar and wind, leads to voltage fluctuations, necessitating advanced control strategies to maintain stability. Additionally, the displacement of traditional synchronous generators by inverter-based DERs reduces system inertia, posing severe frequency stability challenges that require innovative solutions like synthetic inertia and fast frequency response mechanisms. Transient stability issues are also exacerbated by DER integration, highlighting the need for advanced inverter controls and enhanced fault ride-through capabilities. Energy storage systems (ESS) are identified as crucial for buffering the variability of renewable DERs, providing essential services such as frequency regulation and voltage support. However, high costs and scalability issues remain barriers to widespread ESS adoption. The study underscores the importance of supportive regulatory and policy frameworks in facilitating the seamless integration of DERs while maintaining grid stability. Effective policies that promote smart grid technologies and DER-friendly regulations are essential for ensuring a stable, resilient, and sustainable power grid. This research contributes to a deeper understanding of the complex dynamics introduced by DERs and offers insights into developing robust strategies to address stability challenges in modern power systems.

The insertion of renewable and distributed energy resources and the use reduction of fossil-fuel-based ones have been in the spotlight of studies of the scientific community in general. Aiming at an efficient connection interface for these resources and at the reduction of distribution losses, the employment of a Low Voltage Direct Current distribution system (LVDC) is considered in this study. For this purpose, the modification of a pristine Low Voltage Alternate Current distribution system (LVAC) to a DC one is proposed without network’s rerouting and with minimal adaptations. Both systems were implemented in the electric power distribution system simulator, OpenDSS (open-source code), and compared to their losses, energy transferred to the loads, voltage drops along the line, as well as number of conductors required for their operation.

Microgrids can serve to integrate distributed energy resources (DERs) and controllable loads in a smarter and more reliable fashion. The operation of residential microgrids with DER during islanded situation is of great significance to both customers and utility providers. This paper proposes two control strategies for a residential microgrid that has a shared energy storage (ES) in islanded mode. With ES being the only energy resource, these strategies rationally allocate energy over the islanded time period and also regulate loads to coordinate with ES. A simulation testbed based on household load models for typical residential devices is utilized. Test cases for islanded-situation days using these control strategies are simulated for validation. Finally, simulation results of control signals and resulting load profiles are shown and analyzed. These two control strategies are proved to be effective for the energy management of a residential microgrid during islanded situation.