Energy management of micro-grid using cooperative game theory

Micro-grid (MG) has been introduced as a low voltage and a very small power system connected to a distribution grid through the point of common coupling. It consists of distributed energy resources (DERs) such as solar Photovoltaic (PV), wind turbine, fuel cell, etc.), interconnected load and energy storage sources. It can operate in grid-connected (i.e. when connected to the main grid) or islanded (i.e. when not connected to the main grid) mode. It has an advantage of utilizing low carbon sources and the possibility of its use in the remote local environment, which means that the transmission infrastructures and their associated costs may be deferred. Although there has been a proliferation of optimization methods of energy management in the MG, most of these methods consider self-interest of the players in profit distribution. Moreover, only a few of them consider a fair profit distribution using Nash bargaining solution (NBS) (i.e. when utility function is linear) leading to even profit distribution and high degree of dissatisfaction. For the MG to achieve better economic outcomes, a novel method based on weighted fair energy management among the participants (i.e. building of different types, such as residential buildings, schools, and shops) is proposed. The novelty of the proposed method lies in the new profit sharing method to favour certain participant by assigning a weight to each participant with cooperative game theory (CGT) approach using generalized Nash bargaining solution (GNBS). The proposed approach achieves a fair (reasonable or just) profit allocation with negotiating power indicator. In this work, a case study of six different participant sites is proposed using the CGT method of energy management. The proposed method is able to cope with the drawbacks of the existing independent method, which negotiate directly with other participants for selfish profit distribution. It is demonstrated that the independent method results in (1) a reduction in the profit of each participant of MG when compared with CGT approach and (2) the variation of transfer prices in some participants having profit below the specified lower bound profit since the method does not take into consideration the lower profit bounds. The use of CGT method (i.e. when participants form a coalition) to finding multi-partner profit level subject to specified lower bounds is demonstrated. This results in (1) increase in the profit of the MG participants (2) maintaining the profit level of all the participants above status-quo profit (lower specified profit bounds) with variation in transfer prices and (3) allowing certain participant to be favoured by assigning higher negotiating power to such participant. To achieve the optimal solution in the proposed method, a teaching-learning-based optimization (TLBO) algorithm is presented to efficiently solve the problem. For TLBO algorithm, no specific control parameters are needed except the number of generations and population size. This is in contrast with other heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) that require other control parameters (i.e. GA requires selection and crossover operation, while PSO makes use of social parameters and cognitive weight). To demonstrate the effectiveness of the proposed TLBO method, the profit allocations are tested in the grid-connected and the islanded mode using both the CGT and the independent method. In this work, the proposed TLBO method is compared with one traditional method, i.e. Lambda iteration method and two heuristic methods, i.e. PSO and GA. Thus, by using TLBO a considerable amount of computation time is saved. Using the same parameter setting for all the heuristic algorithms used, 20 trials are performed to be able to compare the quality of solution and convergence characteristics. The investigation reveals that TLBO gives the highest quality solutions and better convergence characteristics compared to PSO and GA

Rightsizing the Design of a Hybrid Microgrid

17 USC 105 interim-entered record; under review.The article of record as published may be found at http://dx.doi.org/10.3390/en14144273Selecting the sizes of distributed energy resources is a central planning element when designing a microgrid. Decision makers may consider several important factors, including, but not limited to, capacity, cost, reliability and sustainability. We introduce a method for rightsizing capacity that presents a range of potential microgrid design solutions, allowing decision makers to weigh their upsides and downsides based on a variety of measurable factors. We decouple component-specific modeling assumptions, energy management system logic and objective measurements from our simulation-based nested binary search method for rightsizing to meet power loads. In doing so, we develop a flexible, customizable and extensible approach to microgrid design planning. Aspects which have traditionally been incorporated directly in optimization-centric frameworks, such as resilience and reliability, can be treated as complementary analyses in our decoupled approach. This enables decision makers to gain exposure to a wide range of relevant information and actively participate in the microgrid design assessment process.Energy System Technology Evaluation ProgramOffice of Naval ResearchNaval Facilities Engineering Systems Command (NAVFAC)Naval Postgraduate Schoo

Reviewing energy system modelling of decentralized energy autonomy

Research attention on decentralized autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of publications and the share of these studies in the corpus of energy system modelling literature. This paper shows the status quo and future modelling needs for research on local autonomous energy systems. A total of 359 studies are roughly investigated, of which a subset of 123 in detail. The studies are assessed with respect to the characteristics of their methodology and applications, in order to derive common trends and insights. Most case studies apply to middle-income countries and only focus on the supply of electricity in the residential sector. Furthermore, many of the studies are comparable regarding objectives and applied methods. Local energy autonomy is associated with high costs, leading to levelized costs of electricity of 0.41 $/kWh on average. By analysing the studies, many improvements for future studies could be identified: the studies lack an analysis of the impact of autonomous energy systems on surrounding energy systems. In addition, the robust design of autonomous energy systems requires higher time resolutions and extreme conditions. Future research should also develop methodologies to consider local stakeholders and their preferences for energy systems

Towards near 100% renewable power systems: Improving the role of distributed energy resources using optimization models

The envisioned near 100 % renewable Power Systems, crucial in attaining the sustainability goals aspired by society, will call for the active and multifaceted participation of all the actors involved in the energy systems. Time-varying renewable energy systems (vRES), such as solar photovoltaic (PV) and wind, will play a decisive role in meeting the ambitious renewable targets. This is due to the large availability of natural resources and the rapid decrease in investment costs observed in the last two decades. In fact, most of the scenarios to achieve near 100% RES in Europe strongly rely on these two energy sources. However, the high temporal and spatial variability of the power generated by these technologies represents a challenge for preserving the high-security standards of supply, quality of service, and the robustness of current power systems, especially with the foreseen contributions from vRES. With an emphasis on the vital role these renewable technologies play in this process, this work aims to develop new methods and tools that may assist different players in different stages of this transition. The three leading contributions are: 1. A Multiyear Expansion-Planning Optimization Method (MEPOM) to be used in the planning processes carried out by system operators and governmental entities. 2. An Optimal Design and Sizing of Hybrid Power Plants (OptHy) decision-support tool to be used in accessing investment decisions and other managing actions led by renewable power plant owners and investors. 3. A Decision-aid Algorithm for Market Participation and Optimal Bidding Strategy (OptiBID) that market agents may adopt to operate and value their renewable energy assets in the electricity markets

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon