Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field. <s> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> According to operation characteristics of a microgrid with fluctuant energy, such as solar energy, wind energy and alike, a multi-objective economic dispatching model for microgrid is proposed. These objectives are to minimize the production cost, customer outage cost and emission pollution cost. Taking into account characteristics of the proposed model, the maximum fuzzy satisfaction degree method is used for fuzzy multi-objective optimization problem to transform the multi-objective optimization problem into a non-linear single-objective optimum problem, which is solved by using particle swarm algorithm at the hour-level commitment of DGs and the output power distributions of DGs. Simulation results show that multi-objective model is better than single-objective model in reflecting the actual operation characteristics of microgrid more precisely and reaching a higher reliability and environmental benefit at as low operation cost as possible. <s> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Energy management in Smart Home environments is undoubtedly one of the pressing issues in the Smart Grid research field. The aim typically consists in developing a suitable engineering solution able to maximally exploit the availability of renewable resources. Due to the presence of diverse cooperating devices, a complex model, involving the characterization of nonlinear phenomena, is indeed required on purpose. In this paper an Hybrid Soft Computing algorithmic framework, where genetic, neural networks and deterministic optimization algorithms jointly operate, is proposed to perform an efficient scheduling of the electrical tasks and of the activity of energy resources, by adequately handling the inherent nonlinear aspects of the energy management model. In particular, in order to address the end-user comfort constraints, the home thermal characterization is needed: this is accomplished by a nonlinear model relating the energy demand with the required temperature profile. A genetic algorithm, based on such model, is then used to optimally allocate the energy request to match the user thermal constraints, and therefore to allow the mixed-integer deterministic optimization algorithm to determine the remaining energy management actions. From this perspective, the ability to schedule the tasks and allocate the overall energy resources over a finite time horizon is assessed by means of diverse computer simulations in realistic conditions, allowing the authors to positively conclude about the effectiveness of the proposed approach. The degree of realism of the simulated scenario is confirmed by the usage of solar energy production forecasted data, obtained by means of a neural-network based algorithm which completes the framework. <s> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Abstract The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed. <s> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new microgrid planning approaches are pointed out. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Providing a cost-efficient and reliable energy is one of the main issues in human societies of the 21st century. In response to this demand, new features of micro grid technology have provided huge potentials, specifically by the capability of having an interactive coordination between energy suppliers and consumers. Accordingly, this paper offers an improved model for achieving an optimal Demand Response programing. To solve the proposed multi-objective optimization problem, Artificial Bee Colony algorithm and quasi-static technique are utilized. The considered objectives in this paper are minimizing the overall cost of energy consumption and also improving the technical parameters of micro grid over a time horizon. This optimization is subject to several constraints such as satisfying the energy balance and the operating constraints of each energy supply sources. Manageable load or load as source is another enabling feature existing in smart energy networks, which is considered in this paper and its effect on cost reduction and reliability improvement is studied. Trying to examine the performance of the proposed Demand Response Programing in real conditions, the uncertainties are also analyzed by stochastic methods. The results show significant improvements which are obtained by applying just intelligent programming and management. <s> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Abstract In this paper, a new methodology has been discussed on economical power management of a grid-connected CHP system which consists of a wind turbine, photovoltaic array, polymer exchange membrane fuel cell and an electrolyzer. Hence, the paper also attempts to present the thorough modeling of energy management of the hybrid system in the form of a nonlinear optimization problem; through which offline energy management is regarded to use the available options in order to determine the role of each generator in producing heat, power and hydrogen in the system under study. In addition to this, variable tariffs of daily exchange with local grid have been taken into account in different operation conditions so as to reach real utilization requirements. Based on these facts, choosing the most suitable model for each element of the hybrid system has been considered in regard to their dynamic behavior. The proposed hybrid system is to supply the electrical and thermal demands of renewable energy laboratory of Shahid Bahonar University of Kerman (SBUK). The economical energy management is comprehensively studied and surveyed in the form of various scenarios while the hybrid system is connected to the grid. <s> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Scalarization techniques are a popular method for articulating preferences in solving multi-objective optimization problems. These techniques, however, have so far proven to be ill-suited in finding a preference-driven approximation that still captures the Pareto front in its entirety. Therefore, we propose a new concept that defines an optimal distribution of points on the front given a specific scalarization function. It is proven that such an approximation exists for every real-valued problem irrespective of the shape of the corresponding front under some very mild conditions. We also show that our approach works well in obtaining an equidistant approximation of the Pareto front if no specific preference is articulated. Our analysis is complemented by the presentation of a new algorithm that implements the aforementioned concept. We provide in-depth simulation results to demonstrate the performance of our algorithm. The analysis also reveals that our algorithm is able to outperform current state-of-the-art algorithms on many popular benchmark problems. <s> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> This paper presents a probabilistic Unit Commitment (UC) model for optimal scheduling of wind power, load forecasts and controllability of vehicles in a microgrid using a stochastic programming framework. The microgrid is made up of microturbines, wind turbine, boiler, Plug-in Electric Vehicles (PEVs), thermal storage and battery storage. The proposed model will help the power grid operators with optimal day- ahead planning even with variable operating conditions in respect of load forecasts, controllability of vehicles and wind generation. A set of valid scenarios is assigned for the uncertainties of wind sources, load and PEVs and objective function in the form of expected value. The objective function is to maximize the expected total profit of the UC schedule for the set of scenarios from the viewpoint of microgrid management. The probabilistic unit commitment optimizes the objective function using Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the stochastic modelling and make a comparison with a simple deterministic one, a typical microgrid is used as a case study. The results can be used to evaluate the effect of integration of PEVs on the economic operation of the microgrid. The results also confirm the necessity to consider the key uncertainties of the microgrid; otherwise the results could overly misrepresent the real world operation of the system. <s> BIB011 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> This study deals with optimal resources planning of energy resources in smart home energy system (SHES), where PV (Photovoltaic) panel, battery and electrical heater (EH) are employed to meet all electrical and thermal demands. The advantage of this configuration is avoiding the use of two different energy carriers including electricity and natural gas in downstream users, in addition, reduction of SHES operation cost. An energy management controller (EMC) based on invasive weed optimization (IWO) algorithm is designed in order to optimize the energy routing and scheduling of different resources to obtain an efficient look-up table, by which the power generation of each energy resource is determined for all hourly time intervals. Different electricity tariffs and all operational constraints of energy resources are considered. Several scenarios are defined and the impacts of system components on SHES operation cost are investigated. Moreover, feasibility study of the proposed SHES based on different electricity and gas prices is conducted. It is observed that the proposed optimal scheduling based on IWO algorithm outperforms other approaches. Furthermore, it is shown that self-discharge rate of the battery, affects the system operational performance, especially time of charging and discharging of the battery. <s> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions. <s> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> The optimization of operating times and operation modes of devices and systems that consume or generate electricity in buildings by building energy management systems promises to alleviate problems arising in today’s electricity grids. Conflicting objectives may have to be pursued in this context, giving rise to a multi-objective optimization problem. This paper presents the optimization of appliances as well as heating and air-conditioning devices in two distinct settings of smart buildings, a residential and a commercial building, with respect to the minimization of energy costs, CO2 emissions, discomfort, and technical wearout. We propose new encodings for appliances that are based on a combined categorization of devices respecting both, the optimization of operating times as well as operation modes, e.g., of hybrid devices. To identify an evolutionary algorithm that promises to lead to good optimization results of the devices in our real-world lab environments, we compare four state-of-the-art algorithms in realistic simulations: ESPEA, NSGA-II, NSGA-III, and SPEA2. The results show that ESPEA and NSGA-II significantly outperform the other two algorithms in our scenario. <s> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> To enable the efficient utilization of energy carriers and the successful integration of renewable energies into energy systems, building energy management systems (BEMS) are inevitable. In this article, we present a modular BEMS and its customizable architecture that enable a flexible approach towards the optimization of building operation. The system is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation. Evaluations in realistic scenarios show the ability of the BEMS to increase energy efficiency, self-consumption, and self-sufficiency as well as to reduce energy consumption and costs by an improved scheduling of the devices that considers all energy carriers in buildings as well as their interdependencies. <s> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> Approaches: Nature-based meta-heuristic methods <s> Abstract The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24 h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation. <s> BIB016
Meta-heuristic methods are high level procedures to find a sufficiently good solution by exploiting some features of the search-space. A significant advantage of meta-heuristic algorithms is to be problem-agnostic, that means, they do not require special knowledge of the problem to be solved and can be applied to a wide range of different optimization problems BIB001 . Meta-heuristics may combine multiple lowlevel heuristics and make use of their domain-specific knowledge in order to find a better near-optimal solutions BIB007 . Among others, nature-based meta-heuristics are an active area of research BIB006 . A first group of nature-based meta-heuristics is inspired by the behavior of a group of animals interacting with each other and with the environment, and it is referred to as Swarm intelligence BIB006 . In Particle Swarm Optimization (PSO), solutions are called particles and are areas in a search space of the given problem. Each particle moves toward the best solutions according to its current and previous best positions, as well as the knowledge of the other particles in the swarm (i.e., the entire group). Once all particles have been moved, the next iteration starts BIB002 . PSO is applied in Moghaddas BIB011 to solve a non-linear profit maximization problem and in BIB004 to solve a MO problem, after transforming it into a SO one by introducing membership degree variable (see Section 5.1). In BIB016 , a decomposed-PSO algorithm is proposed by combining this method with the interior-point method to solve a non-convex non-linear cost minimization. Another swarm-intelligence meta-heuristic is the Artificial Bee Colony (ABC) algorithms, that mimics the intelligent foraging behavior of honey bees. There are three groups of bees in the colony, which look for the best sources of nectar and share information in order to find the optimal path for the next iteration, until some requirements criteria are met. This algorithm is applied in BIB008 to solve a nonlinear optimization problem, both its deterministic and stochastic versions. The nonlinear minimization cost problem in BIB009 is solved by applying the physical-inspired meta-heuristic Gravitational Search Algorithm (GSA). The GSA is based on the law of gravity and mass interaction BIB003 ). The group of potential solutions is represented as a set of objects forming a gravitational systems. The objects interact among each other as masses according to their characteristics, namely position, gravitational mass, and inertia mass. At each iteration of the algorithm, masses are evaluated and their position is determined as the result of attraction forces among each others. System parameters such as the gravitational mass and the gravitational constant are updated accordingly. The procedure is repeated until a stopping criterion is met BIB009 . The Invasive Weed Optimization (IWO) algorithm used in BIB012 is a nature-based meta-heuristic, inspired from the growth of weed plants. The algorithm is based on the iteration of three consecutive processes: distribution of seeds, growth of weed plant, and reproduction. The production of seeds depends on the fitness of the weed plant, so that only plants with the best fitness are allowed to spread. The optimized solution is hence represented by the weed plant with the best fitness value  . Evolutionary Algorithms (EAs) are based on Darwin's theory of natural selection within a population. Starting from an initial population, i.e., a group of potential solutions, crossover and mutation are performed to obtain the offspring population. Solutions are evaluated according to a fitness function, which varies with the defined problem, and a portion of them is selected to generate a new offspring population, by a combination of genetic operators. The procedure is repeated until a termination criteria is met (Marler and Arora, 2004). Among others, Genetic Algorithm (GA) have gained popularity in several areas and have been applied to both SO and MO problems. In particular, the non-dominant sorting genetic algorithm (NSGA-II) proposed by  is used in BIB013 and BIB014 to get the Pareto frontier with trade-offs between two or more objective functions. NSGA-III is the successor to NSGA-II and it is particularly efficient in solving problems in three or higher dimensions, as shown in BIB014 . A GA-based algorithm is used also in BIB015  to solve a SO problem. It operates on the global optimization problem that integrates all sub-problems resulting from the dynamic programming approach. In BIB005 , a GA is used to solve a NLP, describing the thermal dynamics of a household. Two other algorithms used in BIB014 to solve a MO problem are SPEA2  and ESPEA BIB010 . The former belongs to the EAs, while the latter is inspired by the physical phenomenon of electrostatic potential energy.