Energy Management Of Grid-connected Microgrid Research Articles

An online cooperative control strategy for enhancing microgrid participation into electricity market

This paper develops a cooperative control methodology for the online energy management of grid-connected microgrids. The main aim of this methodology is to actively manage the active power outputs of all dispatchable energy sources available within the microgrid as well as the power exchanged with the utility grid so as to match the total load demand at the minimum operating cost. This implies to solve a constrained multi-objective dynamic optimization problem aimed at minimizing the total microgrid operating costs and ensuring the real-time balance within the microgrid in compliance with its technical-operational constraints. Lyapunov's theorem using sensitivity theory is adopted to solve this optimization.To test the performances of the proposed control methodology, several computer simulations corresponding to different operating scenarios have been conducted on the PrInCE Lab experimental microgrid built at the Polytechnic University of Bari.

Energy Management of Grid-Connected Microgrids Using an Optimal Systems Approach

Microgrids (MGs) are a growing energy industry segment and represent a paradigm shift from remote central power plants to more localized distributed generation. Controlling MGs represents a challenge mainly due to their complexity and the different properties each asset in the MG has. Various methods have been proposed to address this challenging problem of MG control. Some of these methods are considered the optimal operation of MG assets. Other works are based on a systems approach and address the scalability and simplicity of synthesizing a MG’s energy management system (EMS). <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variables based logical control strategies, which are practical methods to model control strategies in MGs, can make the control structure more scalable. However, this method is not optimal. On the other hand, Switched Model Predictive Control (S-MPC) is an advanced method utilized to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Nevertheless, its implementation is not straightforward. Therefore, to overcome these existing problems, this paper proposes a novel systems approach method called an extended optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable method developed by combining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable based control method with the S-MPC method. This unique method has demonstrated a significant improvement in optimizing an MG’s energy management and enhanced the adaptation and scalability of a control structure of the MG. Our results show that the proposed extended optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable method: (i) reduces the operational cost of MG by nearly 35%; (ii) reduces the usage of the battery energy storage system by 42%, and (iii) enhances the practicality of photovoltaic (PV) usage by 28%. Our novel extended optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable technique also increases the adaptation and scalability of the control structure of the MG significantly by translating the results of S-MPC to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable method.

Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers

A microgrid comprises renewable and non-renewable power generating sources, controllable loads, energy storage devices and works as a single controllable entity. A gradual shift from conventional internal combustion engine-based vehicles to electric/hybrid electric vehicles has also led to a new load type in the power system. Moreover, demand-side management measures like demand response programs have become popular. This work deals with the optimal coordinated operation of a grid-connected AC microgrid consisting of controllable and uncontrollable power sources, battery storage units, considering plug-in hybrid electric vehicles and demand response programs. Stochastic models of renewable power sources, electric load demand, loads of hybrid electric vehicles (with battery charging characteristic), and grid power price are fed into “Hong’s 2 m point estimate method” embedded optimal operating strategy. The objective is to minimize the cost of operation subject to the satisfaction of technical constraints. A nested stochastic optimization algorithm is implemented to find optimal generation schedule, battery dispatch strategy, and the best incentive value for an incentive-based demand response program. Different charging strategies of hybrid electric vehicles are studied, and their impacts on system operation are investigated. The optimal coordination between a voltage control scheme using a smart transformer with the energy management scheme is also investigated. Simulation studies on a thirty-three bus test system prove the efficacy of the proposed algorithm. The proposed coordinated optimal operating strategy reduces the operating cost by 17.53%∼17.74%. The system loss also reduces by 29.49%∼31.36%.

Energy Management System for Hybrid Microgrids

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

Real-time Energy Management of Grid-connected Microgrid with Flexible and Delay-tolerant Loads

For optimal operation of microgrids, energy management is indispensable to reduce the operation cost and the emission of conventional units. The goals can be impeded by several factors including uncertainties of market price, renewable generation, and loads. Real-time energy management system (EMS) can effectively address uncertainties due to the online information of market price, renewable generation, and loads. However, some issues arise in real-time EMS as battery-limited energy levels. In this paper, Lyapunov optimization is used to minimize the operation cost of the microgrid and the emission of conventional units. Therefore, the problem is multi-objective and a Pareto front is derived to compromise between the operation cost and the emission. With a modified IEEE 33-bus distribution system, general algebraic modeling system (GAMS) is utilized for implementing the proposed EMS on two case studies to verify its applicability.

Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response

This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity.

A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids

A single-level rule-based model predictive control (RBMPC) scheme is presented for optimizing the energy management of a grid-connected microgrid composed of local production units, renewable energy sources, local loads, and several types of energy storage systems (ESSs). The single-level controller uses two different models that yield different descriptions of the microgrid and use different sampling times. The model with a smaller sampling time provides a more detailed description of the microgrid, in order to keep track of the fast dynamics, while the model with a higher sampling time provides a less detailed description and is used for making long-term predictions when it is not needed anymore to track the fast dynamics. Moreover, we propose a novel RBMPC method that assigns the value to the binary decision variables in the hybrid microgrid model, e.g., ON or OFF status of the generators and charging or discharging mode of ESSs, through if–then–else rules, which rely on the price of electricity and the local net imbalance. The standard method of applying model predictive control (MPC) to a hybrid model results in a mixed-integer linear programming (MILP) problem. Our proposed rule-based method is able to convert the standard MILP problem into a linear one. We compare our approach through simulations to the MILP approach and show that our method yields almost no loss in performance while providing a significant reduction in the computation time.

Modified PSO algorithm for real-time energy management in grid-connected microgrids

In real-time energy management of a converter-based microgrid, it is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. The complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function is proposed in order to obtain the best cost function. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 h. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.