Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

Experimental validation of optimal real-time energy management system for microgrids

Nowadays, power production, reliability, quality, efficiency and penetration of renewable energy sources are amongst the most important topics in the power systems analysis. The need to obtain optimal power management and economical dispatch are expressed at the same time. The interest in extracting an optimum performance minimizing market clearing price (MCP) for the consumers and provide better utilization of renewable energy sources has been increasing in recent years. Due to necessity of providing energy balance while having the fluctuations in the load demand and non-dispatchable nature of renewable sources, implementing an energy management system (EMS) is of great importance in Microgrids (MG). The appearance of new technologies such as energy storage (ES) has caused increase in the effort to present new and modified optimization methods for power management. Precise prediction of renewable energy sources power generation can only be provided with small anticipation. Hence, for increasing the efficiency of the presented optimization algorithm in large-dimension problems, new methods should be proposed, especially for short-term scheduling. Powerful optimization methods are needed to be applied in such a way to achieve maximum efficiency, enhance the economic dispatch as well as provide the best performance for these systems. Thus, real-time energy management within MG is an important factor for the operators to guarantee optimal and safe operation of the system. The proposed EMS should be able to schedule the MG generation with minimum information shares sent by generation units. To achieve this ability, the present thesis proposes an operational architecture for real time operation (RTO) of a MG operating in both islanding and grid-connected modes. The presented architecture is flexible and could be used for different configurations of MGs in different scenarios. A general formula is also presented to estimate optimum operation strategy, cost optimization plan and the reduction of the consumed electricity combined with applying demand response (DR). The proposed problem is formulated as an optimization problem with nonlinear constraints to minimize the cost related to generation sources and responsive load as well as reducing MCP. Several optimization methods including mixed linear programming, pivot source, imperialist competition, artificial bee colony, particle swarm, ant colony, and gravitational search algorithms are utilized to achieve the specified objectives. The main goal of the thesis is to validate experimentally the design of the real-time energy management system for MGs in both operating modes which is suitable for different size and types of generation resources and storage devices with plug-and-play structure. As a result, this system is capable of adapting itself to changes in the generation and storage assets in real-time, and delivering optimal operation commands to the assets quickly, using a local energy market (LEM) structure based on single side or double side auction. The study is aimed to figure the optimum operation of micro-sources out as well as to decrease the electricity production cost by hourly day-ahead and real time scheduling. Experimental results show the effectiveness of the proposed methods for optimal operation with minimum cost and plug-and-play capability in a MG. Moreover, these algorithms are feasible from computational viewpoints while having many advantages such as reducing the peak consumption, optimal operation and scheduling the generation unit as well as minimizing the electricity generation cost. Furthermore, capabilities such as the system development, reliability and flexibility are also considered in the proposed algorithms. The plug and play capability in real time applications is investigated by using different scenarios

Cooling load estimation using machine learning techniques

Estimating cooling loads in heating, ventilation, and air-conditioning (HVAC) systems is a complex task. This is mainly due to its dependence on numerous factors which are both intrinsic and extrinsic to buildings. These include climate, forecasts, building material, fenestration etc. In addition, these factors are non-linear and time-varying. Therefore, capturing the effect of these parameters on the cooling load is a complex task. This investigation combines forward modelling, i.e., physics based model simulated using energyPlus with deep-learning techniques to build a cooling load estimator. The forward model captures all the time-varying factors influencing the cooling loads. We use the long short-term memory (LSTM), a deep-learning method to provide forecasts of cooling loads. The advantage of the proposed approach is that cooling load estimations can be provided in real-time thus providing sort of soft-sensor for estimating cooling loads in buildings. The proposed approach is illustrated on a building of suitable scale and our results demonstrates the ability of the tool to provide forecasts

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Thermal Characteristics and Safety Aspects of Lithium-Ion Batteries: An In-Depth Review

This paper provides an overview of the significance of precise thermal analysis in the context of lithium-ion battery systems. It underscores the requirement for additional research to create efficient methodologies for modeling and controlling thermal properties, with the ultimate goal of enhancing both the safety and performance of Li-ion batteries. The interaction between temperature regulation and lithium-ion batteries is pivotal due to the intrinsic heat generation within these energy storage systems. A profound understanding of the thermal behaviors exhibited by lithium-ion batteries, along with the implementation of advanced temperature control strategies for battery packs, remains a critical pursuit. Utilizing tailored models to dissect the thermal dynamics of lithium-ion batteries significantly enhances our comprehension of their thermal management across a wide range of operational scenarios. This comprehensive review systematically explores diverse research endeavors that employ simulations and models to unravel intricate thermal characteristics, behavioral nuances, and potential runaway incidents associated with lithium-ion batteries. The primary objective of this review is to underscore the effectiveness of employed characterization methodologies and emphasize the pivotal roles that key parameters—specifically, current rate and temperature—play in shaping thermal dynamics. Notably, the enhancement of thermal design systems is often more feasible than direct alterations to the lithium-ion battery designs themselves. As a result, this thermal review primarily focuses on the realm of thermal systems. The synthesized insights offer a panoramic overview of research findings, with a deeper understanding requiring consultation of specific published studies and their corresponding modeling endeavors

The Optimization of Microgrids Operation through a Heuristic Energy Management Algorithm

The concept of microgrid was first introduced in 2001 as a solution for reliable integration of distributed generation and for harnessing their multiple advantages. Specific control and energy management systems must be designed for the microgrid operation in order to ensure reliable, secure and economical operation; either in grid-connected or stand-alone operating mode. The problem of energy management in microgrids consists of finding the optimal or near optimal unit commitment and dispatch of the available sources and energy storage systems so that certain selected criteria are achieved. In most cases, energy management problem do not satisfy the Bellman's principle of optimality because of the energy storage systems. Consequently, in this paper, an original fast heuristic algorithm for the energy management on stand-alone microgrids, which avoids wastage of the existing renewable potential at each time interval, is presented. A typical test microgrid has been analysed in order to demonstrate the accuracy and the promptness of the proposed algorithm. The obtained cost of energy is low (the quality of the solution is high), the primary adjustment reserve is correspondingly assured by the energy storage system and the execution runtime is very short (a fast algorithm). Furthermore, the proposed algorithm can be used for real-time energy management systems

Energy management system of hybrid microgrid with energy storage

The economic scheduling of the generation units is playing a significant role in the energy management of the hybrid stand alone microgrid. Energy storage is an increasingly important part of the renewable energy sector because of the need to store power during peak production times for use in off-peak periods. This paper describes an energy management system (EMS) for an islanded microgrid (MG) comprising wind energy conversion system (WECS),photovoltaic (PV), energy storage (ES) system, and microturbine (MT) for calculating the battery charging price (BCP) factor. To reach this objective, firstly the battery system has been modeled using the presented equations then various scenarios is applied by technically limited such as the power, voltage and current applied to charge and discharge of the battery.Postprint (published version

An optimal energy management system for islanded Microgrids based on multi-period artificial bee colony combined with Markov Chain

The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach

Distributed Smart Decision-Making for a Multimicrogrid System Based on a Hierarchical Interactive Architecture

In this paper, a comprehensive real-time interactive energy management system (EMS) framework for the utility and multiple electrically coupled MGs is proposed. A hierarchical bi-level control scheme (BLCS) with primary and secondary level controllers is applied in this regard. The proposed hierarchical architecture consists of sub-components of load demand prediction, renewable generation resource integration, electrical power-load balancing, and responsive load demand. In the primary level, EMSs are operating separately for each microgrid (MG) by considering the problem constraints, power set-points of generation resources, and possible shortage or surplus of power generation in the MGs. In the proposed framework, minimum information exchange is required among MGs and the distribution system operator. It is a highly desirable feature in future distributed EMS. Various parameters such as load demand and renewable power generation are treated as uncertainties in the proposed structure. In order to handle the uncertainties, Taguchi's orthogonal array testing approach is utilized. Then, the shortage or surplus of the MGs power should be submitted to a central EMS in the secondary level. In order to validate the proposed control structure, a test system is simulated and optimized based on multiperiod imperialist competition algorithm. The obtained results clearly show that the proposed BLCS is effective in achieving optimal dispatch of generation resources in systems with multiple MGs

A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions