Energy management for user’s thermal and power needs: A survey <s> Fuel price <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> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> We propose a generic mixed integer linear programming model to minimize the operating cost of a residential microgrid. We model supply and demand of both electrical and thermal energy as decision variables. The modeled microgrid is operated in grid-connected mode. It covers solar energy, distributed generators, energy storages, and loads, among them controllable load jobs released by home appliances and electric vehicles. We propose a model predictive control scheme to iteratively produce a control sequence for the studied microgrid. Our case study reveals the performance of minimum cost control by comparison with benchmark control policies. We consider three price scenarios in our analyses which include two market-based scenarios. Numerical results from our study indicate savings in annual operating cost between 3.1 and 7.6 percent. <s> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Aligned towards net zero energy building goals, building energy management systems will consider energy utilization efficiency improvement, energy cost reduction and renewable energy technology utilization to serve the local energy loads in building structures with dispersed resources. The distributed management of building energy system in this paper describes a semi-centralized decision making methodology using multi-agent systems for building energy management system in electrical, heating and cooling energy zones with combined heat and power system optimization aimed at improving energy efficiency and reducing energy cost. The semi-centralized decision making process will be implemented in a case study to pursue minimum energy cost. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> A growing number of households are seeking energy autonomy and economic benefits by installing micro-CHP and PV generators, as well as battery storage units in their so-called smart homes. An option to further increase benefits, is to install a community microgrid and coordinate smart homes intelligently. To quantify this increase, we apply numerical simulations using real-world data for household loads in a temporal resolution of 15-minutes. In systems consisting of CHP-units, the degree of electricity autonomy rises from 50% to 80% through installing a microgrid, allowing lucrative CHP operation. In PV-based systems, the benefits are fewer and if battery storage is installed additionally, they almost disappear completely. As a consequence, intelligently managed microgrids are as valuable option for the integration of microgeneration as long as decentralized battery storage is not profitable and thus not employed. <s> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Micro-grid technology provides an opportunity and a desirable infrastructure for improving efficiency of electrical and thermal energy consumption. The key to improve electrical and thermal energy efficiency in operation is to coordinate and optimize the operation of various energy resources and loads. In this paper, a typical micro-grid consisting of photovoltaic and wind turbine as renewable resources and diesel generator, combined heat and power and boiler as other Supply resources and battery as system's storage is considered. The objective function was minimizing overall cost of electrical and thermal energy during 24 hours. With regards to demand and supply balance and operation constraints of every energy source and storage and also considering demand response plans, the objective function was minimized by specifying each unit's production or storage. Simulation results show that, a significant save of money and fuel can be achieved through scheduling and control of various energy supply and storage resources. <s> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Microgrid works as a local energy provider for domestic buildings to reduce energy expenses and gas emissions by utilising distributed energy resources (DERs). The rapid advances in computing and communication capabilities enable the concept smart buildings become possible. Most energy-consuming household tasks do not need to be performed at specific times but rather within a preferred time. If these types of tasks can be coordinated among multiple homes so that they do not all occur at the same time yet still satisfy customers’ requirement, the energy cost and power peak demand could be reduced. In this paper, the optimal scheduling of smart homes’ energy consumption is studied using a mixed integer linear programming (MILP) approach. In order to minimise a 1-day forecasted energy consumption cost, DER operation and electricity-consumption household tasks are scheduled based on real-time electricity pricing, electricity task time window and forecasted renewable energy output. Peak demand charge scheme is also adopted to reduce the peak demand from grid. Two numerical examples on smart buildings of 30 homes and 90 homes with their own microgrid indicate the possibility of cost savings and electricity consumption scheduling peak reduction through the energy consumption and better management of DER operation. <s> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> This paper addresses a hybrid renewable system that consists of a micro-Combined Cooling Heat and Power (CCHP) unit and a solar energy conversion device. In addition to a traditional PV system, a High Concentrator Photovoltaic (HCPV) device, the design of which is suitable for building integration application, was also modelled and embedded in the hybrid system. The work identifies the optimal management strategies for the hybrid renewable system in an effort to minimise the primary energy usage, the carbon dioxide emissions and the operational costs for variable electricity prices that result from the day-ahead electricity market. An "ad hoc" model describes the performance of the HCPV module, PV and Internal Combustion Engine, whilst the other units were simulated based on their main characteristic parameters. The developed algorithm was applied to three different building typologies. The results indicate that the best configuration is the hybrid renewable system with PV, which can provide a yearly primary energy reduction of between 20% and 30% compared to separate production. The hybrid renewable system with HCPV becomes competitive with the PV technology when the level of solar radiation is high. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Intermittent generation is challenging for the existing power systems, which have difficulties in following varying electricity production. To help the generation and demand be in balance, demand response (DR) provides one promising approach. Among many DR applications, residential micro-CHP systems can be advantageous, as these systems typically comprise storage in addition to a controllable cogeneration unit. Current state-of-the-art control strategies for residential micro-CHP systems utilize time-varying electricity price to optimally operate the system. Thus, such systems have the ability to export or import electricity when either one is beneficial. <s> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Aggregating groups of loads and generators at the same location with centralized control is known as the concept of microgrids. However, if those flexible producers and consumers do not have the ability to balance the variability and uncertainty of RES (renewable energy sources) production within them, from the system perspective they are seen as a source of imbalances and potential problems in maintaining the equilibrium of production and consumption. The papers main goal is to quantify the ability of microgrid components to provide flexibility. This flexibility is analysed from two perspectives, defining two operating principles of each microgrid: independently from the distribution grid and connected, interacting and responding to signals from the upstream system. Following on this, the paper presents two relevant cases. <s> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Energy crisis along with environmental concerns are some principal motivations for introducing “energy hubs” by integrating energy production, conversion and storage technologies such as combined cooling, heating and power systems (CCHPs), renewable energy resources (RESs), batteries and thermal energy storages (TESs). In this paper, a residential energy hub model is proposed which receives electricity, natural gas and solar radiation at its input port to supply required electrical, heating and cooling demands at the output port. Augmenting the operational flexibility of the proposed hub in supplying the required demands, an inclusive demand response (DR) program including load shifting, load curtailing and flexible thermal load modeling is employed. A thermal and electrical energy management is developed to optimally schedule major household appliances, production and storage components (i.e. CCHP unit, PHEV and TES). For this purpose, an optimization problem has been formulated and solved for three different case studies with objective function of minimizing total energy cost while considering customer preferences in terms of desired hot water and air temperature. Additionally, a multi-objective optimization is conducted to consider consumer's contribution to CO2, NOx and SOx emissions. The results indicate the impact of incorporating DR program, smart PHEV management and TES on energy cost reduction of proposed energy hub model. <s> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <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> BIB011 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Nowadays with the emerging of smart micro-grids(SM-Gs) in residential sectors, a large portion of energy consumption can be saved through optimal scheduling of household devices and management of domestic hybrid energy sources. By the aid of such technologies, residential consumers have the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy supply system. This paper presents a novel residential energy management system (REMS) to improve the efficiency of energy consumption in a typical SM-G taking into account minimum cost of energy as well as maximum user's comfort level as competitive objectives. The optimization model is also formulated as a mixed integer nonlinear problem (MINLP) and its performance is tested under different operating scenarios with real data. The simulation results show that the proposed model not only reduces energy consumption costs, but also ensures a comfortable lifestyle for occupants. <s> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> In this study we present a Model Predictive Control (MPC) approach to Energy Management Systems (EMSs) for multiple residential microgrids. The EMS is responsible for optimally scheduling end-user smart appliances, heating systems and local generation devices at the residential level, based on end-user preferences, weather-dependent generation and demand forecasts, electric pricing, technical and operative constraints. The core of the proposed framework is a mixed integer linear programming (MILP) model aiming at minimizing the overall costs of each residential microgrid. At each time step, the computed optimal decision is adjusted according to the actual values of weather-dependent local generation and heating requirements; then, corrective actions and their corresponding costs are accounted for in order to cope with imbalances. At the next time step, the optimization problem is re-computed based on updated forecasts and initial conditions. The proposed method is evaluated in a virtual testing environment that integrates accurate simulators of the energy systems forming the residential microgrids, including electric and thermal generation units, energy storage devices and flexible loads. The testing environment also emulates real-word network medium conditions on standard network interfaces. Numerical results show the feasibility and the effectiveness of the proposed approach. <s> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> The concept of smart grid SG has been introduced to improve the operation of the power systems. In modern structures of power systems, different reasons prompt researchers to suggest integrated analysis of multi-carrier energy systems. Considering synergy effects of the couplings between different energy carriers and utilising intelligent technologies for monitoring and controlling of energy flow may change energy system management in the future. In this paper, we propose a new solution which is entitled ‘smart energy hub’ SEH that models a multi-carrier energy system in a SG. SEH solutions allow homeowners to manage their energy consumption to reduce their electricity and gas bill. We present this concept for a residential customer by an ‘energy management system’ which uses reinforcement learning algorithm and Monte Carlo estimation method for finding a near optimal solution. The simulation results show that by using this concept and then applying the algorithm for a residential customer, running costs are reduced up to 40% while keeping the household owner's desired comfort levels. <s> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract With the emergence of smart grid which has advanced metering infrastructure (AMI) and microgrid running as a local energy provider for dwellings through distributed energy resources (DERs), dwellers have the opportunity to schedule their in-home energy usage by themselves to reduce energy expense. Household tasks along with DER operation are scheduled according to electricity real-time price (RTP) and natural gas fixed price. Various studies have shown that the lack of effective home automation systems and the lack of awareness among users to respond to time-varying prices are two chief obstacles in order to fully exploit the potential benefits of dynamic pricing schemes. This paper drives to handle these problems by proposing an automatic and optimal residential energy consumption scheduling technique which tries to achieve a favorable trade-off between minimizing the energy costs as well as the inconvenience for the operation of both electrical and thermal in a smart home environment. Simulation results show that the proposed scheduling method leads to significant reduction in energy costs for diverse load scenarios with the electricity demand from the grid. Therefore, the deployment of the proposed method is advantageous for both users and utilities. <s> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract A model predictive control (MPC) based coordinated operation framework for a grid-connected residential microgrid with considering forecast errors is presented in this paper. This residential microgrid composes renewable energy resources (e.g., wind and solar), distributed generators (e.g., CHP), energy storages (e.g., battery bank and water tank), electrical vehicle, and smart loads (e.g. HVAC and washing machine). A novel mixed integer linear programming (MILP) problem is optimized at each decision time, on the basis of the short-term forecasts of renewable energy resources generation, load demand, and electricity price. This MILP problem is integrated into a MPC framework to reduce the negative impacts of forecast errors. Case study which considers forecast uncertainties is implemented for evaluating the performance of the proposed method and the traditional method is used. Besides, peak power price mechanism which is used to smooth the power exchanged with external grid is also considered. Moreover, a further sensitivity analysis is realized in order to discuss the impacts of energy storage units on the microgrid operation. Simulation results show that the proposed method is economic and flexible. <s> BIB016 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> The development of technologies such as micro turbines and gas furnaces has been the major driving force towards integration of electricity and natural gas networks in the EH (energy hubs). Besides, the existing power grids are getting smarter by implementing the new generation of information technologies and communication systems. In smart grid, the electricity suppliers can modify the customers' electrical load consumption by employing appropriate scheduling schemes such as DR (demand response) programs. In this paper, we consider the S. E. Hubs (smart energy hubs) framework, in which the customers can use EMS (energy management system) to access to the electricity and natural gas prices data and wisely manage their daily energy consumption. We extend the existing DR programs to the IDR (integrated demand response) programs with the aim of modifying both electricity and natural gas consumption on the customer side. The interaction between S.E. hubs in the IDR program is formulated as a non-cooperative game. The goal of the IDR game is to maximize the natural gas and electricity utility companies' profit and to minimize the customers' consumption cost. It is shown that the proposed game model is an ordinal potential game with unique Nash equilibrium. Simulations are performed on an energy system with 6 S. E. Hubs, one electricity utility, and one natural gas utility companies. The results confirm that the IDR program can benefit both the customer side, by reducing the electricity and gas consumption cost, and the supplier side, by reducing the peak load demand in the electricity and natural gas load profiles. <s> BIB017 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Residential fuel cell based combined heat and power (FC-CHP) systems have been commercialized and are penetrating the marketed in Japan. In order to utilize FC-CHP systems efficiently, operational strategies are required. Energy interchange among consumers improves efficiency by allowing flexible operation of equipment. An energy management system (EMS), which is installed at the consumer dwelling, can integrate the control and management of energy equipment by using an optimized planning approach that incorporates the energy interchange and can operates equipment whilst adjusting to real conditions. An EMS has been developed, which achieves optimal operation of FC-CHP systems with energy interchange between dwellings in a residential area. Case studies were performed to evaluate the EMS model by using evaluation indices including energy costs, CO2 emissions, and primary energy consumption. The results showed that 1) energy management and energy interchange were effective and contributed to cost reductions, CO2 mitigation, and reduction of primary energy consumption and 2) the developed EMS model achieved optimal operations. <s> BIB018 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> This work describes an implementation of an office and/or personal smart grid for environmentally friendly buildings. These can be equipped with a local energy source (e.g., photovoltaic panels or combined heat-power units), energy storage devices (batteries, electric hot water boilers, heating, and ventilation systems including air conditioning), a building energy management system with sensors (e.g., providing the room temperatures), and household appliances acting as actuators (in general, split into groups of schedulable and non-schedulable ones). The idea behind this work has been to develop an automatic control system which would optimally decide for the end customer when to buy, sell, or store electric energy with the objective to minimize his total costs. At the same time, it fulfills all constraints in terms of the limits on power allowed to be taken from the grid. For the user, besides the optimal scheduling of household appliances and selling energy to the grid whenever it becomes profitable, the developed prototype gives also the opportunity to monitor the local generation, storage, and consumption in real time. On the other side, the electric utility can produce a real-time pricing signal which reflects daily peaks in consumption. Making such a price signal available for this building energy management system will lead to a decrease of power consumption from the grid. In this project, a model predictive control approach to the energy optimization problem in a building has been proposed. Different scenarios have been run and the results are discussed here. <s> BIB019 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <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> BIB020 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <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> BIB021 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <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> BIB022 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> In a multi-energy system, there are different types of dependencies among the energy carriers. Internal dependencies refer to possible changes in the energy source in the presence of energy converters and storage and are managed by the system operator through the control strategies applied to the equipment. External dependencies are due to the choice of the energy supply according to customer preferences, when alternative solutions are available. This paper introduces a new model of the external dependencies within a multi-generation representation based on energy hubs. The external dependencies are addressed through a stochastic model, in order to take into account the possible uncertainty in the customers' decisions. This model is then used to introduce carrier-based demand response, in which the user participates in demand response programmes aimed at promoting the shifting among different energy sources by preserving the service provided to the end users. The results obtained from the new model in deterministic and stochastic cases indicate the appropriateness and usefulness of the proposed approach. <s> BIB023 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Today, as a consequence of the growing installation of efficient technologies, e.g. micro-combined heat and power (micro-CHP), the integration of traditionally separated electricity and natural gas networks has been attracting attentions from researchers in both academia and industry. To model the interaction among electricity and natural gas networks in distribution systems, this paper models a residential customer in a multi-energy system (MES). In this paper, we propose a fully automated energy management system (EMS) based on a reinforcement learning (RL) algorithm to motivate residential customers for participating in demand side management (DSM) programs and reducing the peak load in both electricity and natural gas networks. This proposed EMS estimates the residential customers’ satisfaction function, energy prices, and efficiencies of appliances based on the residential customers’ historical actions. Simulations are performed for the sample model and results depict how much of each energy, i.e. electricity and natural gas, the residential customer should consume and how much of natural gas should be converted in order to meet electricity and heating loads. It is also shown that the proposed RL algorithm reduces residential customer energy bill and electrical peak load up to 20% and 24%, respectively. <s> BIB024 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost and/or emissions, while adhering to technical and commercial constraints, such as network limits and market contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this methodology is also presented. In order to validate the operation of the developed system, a number of experiments were performed using both software and hardware. The results validated the efficient operation of the developed system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy hub, using a hierarchical multi-agent system control structure. <s> BIB025 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Pre-packaged cogeneration systems are popular as lead supply in distributed and isolated energy systems aimed at empowering remote off-grid communities and in supporting eco-villages in nature reserves. Energy cooperatives and independent power producers deploy cogeneration systems in community shared microgrid configurations as building blocks in smart localised multi-carrier energy grids. Small-scale renewable energy and distributed energy resources in the energy mix of multi-carrier combined heat and power microgrids call for the development of intelligent control automation solutions. This paper describes a multi-objective optimization solution where hierarchical digital microgrid control is integrated into solar powered micro-cogeneration. The control solution includes an integrated cost-competitive economic and environmental optimization algorithm customized around the needs of small off-grid isolated village settlements. A secondary stage optimization loop functions as a storage capacity decision support system, to alert the community when operating costs can be reduced through an investment into increased storage capacity for the village microgrid. This cost-aware control optimization algorithm is tested in a rural village microgrid by using parametric computer simulation models of a hybrid residential solar cogeneration system. Digital simulation experiments evaluate the operational plans and operational cost performances of energy management for different storage scenarios at an isolated remote rural location in Africa. <s> BIB026 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract The deployment of innovative energy management (EM) approaches based on systematic modelling and optimisation techniques has received an increasing amount of attention in the last decade. This has been often prompted by more stringent energy policy objectives aiming at reducing carbon emissions, phasing out nuclear plants and promoting overall energy efficiency, while containing both capital and operating costs. In this respect the energy hub concept has proven to be a popular approach for operating technologies and units comprising diversified energy carriers, small-scale production units, storage devices and converter systems. Additionally, developments in communication network and control infrastructure afford the possibility, at least in principle, to actively steer and adjust the load on the demand side of the energy balance, leading to the formulation of demand side management (DSM) techniques. This paper proposes an EM solution that combines the features and advantages of both of the aforementioned approaches, i.e. the energy hub framework and DSM methods. The key idea is to combine the supply-side characteristics of energy hubs with the demand side flexibility yielded by the deployment of DSM schemes. This combined approach is validated on an existing building complex by formalizing its energy supply system as an integrated hub and by modelling its heating demand based on thermodynamic principles. Numerical results based on this experimental setup are presented, illustrating that the combined approach can lead to overall savings typically exceeding 10% compared to a baseline scenario where no EM solution is applied, i.e. where only a rule-based heuristic is employed to control the available energy assets, and underscoring the advantages brought by a systematically integrated modelling and optimisation approach. The proposed solution is thus of interest for a broad host of installations in the residential and commercial domain, and for the latter a specific real-world example has been explicitly considered and analysed. The obtained results are encouraging and warrant further analysis and investigation. <s> BIB027 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Renewable energy resources are often known as cost-effective and lucrative resources and have been widely developed due to environmental-economic issues. Renewable energy utilization even in small scale (e.g., microgrid networks) has attracted significant attention. Energy management in microgrid can be carried out based on the generating side management or demand side management. In this paper, portable renewable energy resource are modeled and included in microgrid energy management as a demand response option. Utilizing such resources could supply the load when microgrid cannot serve the demand. This paper addresses energy management and scheduling in microgrid including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical ones), and portable renewable energy resource (PRER). Operational cost of microgrid and air pollution are considered as objective functions. Uncertainties related to the parameters are incorporated to make a stochastic programming. The proposed problem is expressed as a constrained, multi-objective, linear, and mixed-integer programing. Augmented Epsilon-constraint method is used to solve the problem. Final results and calculations are achieved using GAMS24.1.3/CPLEX12.5.1. Simulation results demonstrate the viability and effectiveness of the proposed method in microgrid energy management. <s> BIB028 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> In this paper, an ontology-driven multi-agent based energy management system (EMS) is proposed for monitoring and optimal control of an integrated homes/buildings and microgrid system with various renewable energy resources (RESs) and controllable loads. Different agents ranging from simple-reflex to complex learning agents are designed and implemented to cooperate with each other to reach an optimal operating strategy for the mentioned integrated energy system (IES) while meeting the system’s objectives and related constraints. The optimization process for the EMS is defined as a coordinated distributed generation (DG) and demand response (DR) management problem within the studied environment and is solved by the proposed agent-based approach utilizing cooperation and communication among decision agents. To verify the effectiveness and applicability of the proposed multi-agent based EMS, several case studies are carried out and corresponding results are presented. <s> BIB029 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> This paper aims to optimize total energy costs in an operational model of a novel energy hub (EH) in a residential area. The optimization problem is set up based on daily load demand (such as electricity, heat, and cooling) and time-of-use (TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic (PV) generation, solar heat exchanger (SHE), and a battery energy storage system (BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price, capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads. <s> BIB030 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> This paper introduces a management model for optimal scheduling of a multi-carrier energy hub. In the proposed hub, three types of assets are considered: dispersed generating systems (DGs) such as micro-combined heat and power (mCHP) units, storage devices such as battery-based electrical storage systems (ESSs), and heating/cooling devices such as electrical heater, heat-pumps and absorption chillers. The optimal scheduling and management of the examined energy hub assets in line with electrical transactions with distribution network is modeled as a mixed-integer non-linear optimization problem. In this regard, optimal operating points of DG units as well as ESSs are calculated based on a cost-effective strategy. Degradation cost of ESSs is also taken into consideration for short-term scheduling. Simulation results demonstrate that including well-planned energy storage options together with optimal scheduling of generating units can improve the economic operation of the multi-carrier energy hub while meeting the system's constraints. <s> BIB031 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> As the installed capacity of wind power is growing, the stochastic variability of wind power leads to the mismatch of demand and generated power. Employing the regulating capability of demand to improve the utilization of wind power has become a new research direction. Meanwhile, the micro combined heat and power (micro-CHP) allows residential consumers to choose whether generating electricity by themselves or purchasing from the utility company, which forms a residential hybrid energy system. However, the impact of the demand response with hybrid energy system contained micro-CHP on the large-scale wind power utilization has not been analyzed quantitatively. This paper proposes an operation optimization model of the residential hybrid energy system based on price response, integrating micro-CHP and smart appliances intelligently. Moreover, a novel load aggregation method is adopted to centralize scattered response capability of residential load. At the power grid level, a day-ahead stochastic economic dispatch model considering demand response and wind power is constructed. Furthermore, simulation is conducted respectively on the modified 6-bus system and IEEE 118-bus system. The results show that with the method proposed, the wind power curtailment of the system decreases by 78% in 6-bus system. In the meantime, the energy costs of residential consumers and the operating costs of the power system reduced by 10.7% and 11.7% in 118-bus system, respectively. <s> BIB032 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract The energy security and environmental problems impel people to explore a more efficient, environment friendly and economical energy utilization pattern. In this paper, the coordinated operation and optimal dispatch strategies for multiple energy system are studied at the whole Micro Energy Grid level. To augment the operation flexibility of energy hub, the innovation sub-energy hub structure including power hub, heating hub and cooling hub is put forward. Basing on it, a generic energy hub architecture integrating renewable energy, combined cooling heating and power, and energy storage devices is developed. Moreover, a generic modeling method for the energy flow of micro energy grid is proposed. To minimize the daily operation cost, a day-ahead dynamic optimal operation model is formulated as a mixed integer linear programming optimization problem with considering the demand response. Case studies are undertaken on a community Micro Energy Grid in four different scenarios on a typical summer day and the roles of renewable energy, energy storage devices and demand response are discussed separately. Numerical simulation results indicate that the proposed energy flow modeling and optimal operation method are universal and effective over the entire energy dispatching horizon. <s> BIB033 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Microgrids are subsystems of the distribution grid operating as a single controllable system either connected or isolated from the grid. In this paper, a novel cooperative model predictive control (MPC) framework is proposed for urban districts comprising multiple microgrids sharing certain distributed energy resources (DERs). The operation of the microgrids, along with the shared DER, are coordinated such that the available flexibility sources are optimised and a common goal is achieved, e.g., minimizing energy exchanged with the distribution grid and the overall energy costs. Each microgrid is equipped with an MPC-based energy management system, responsible for optimally controlling flexible loads, heating systems, and local generation devices based on end-user preferences, weather-dependent generation and demand forecasts, energy prices, and technical and operational constraints. The proposed coordination algorithm is distributed and guarantees constraints satisfaction, cooperation among microgrids and fairness in the use of the shared resources, while addressing the issue of scalability of energy management in an urban district. Furthermore, the proposed framework guarantees an agreed cost saving to each microgrid. The described method is implemented and evaluated in a virtual testing environment that integrates accurate simulators of the microgrids. Numerical experiments show the feasibility, the computational benefits, and the effectiveness of the proposed approach. <s> BIB034 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> With the development of home area network, residents have the opportunity to schedule their power usage at the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, an energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented that considers both energy consumption and generation simultaneously. The proposed model arranges the household electrical and thermal appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. In this model, the home gateway receives the electricity price information as well as the resident desired options in order to efficiently schedule the appliances and shave the peak as well. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, PV panel and a battery over a 24-h period. <s> BIB035 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract With the increasing penetration of distributed renewable energy generation and dynamic electricity pricing schemes, applications for residential demand side management are becoming more appealing. In this work, we present an optimal control model for studying the economic and grid interaction benefits of smart charging of electric vehicles (EV), vehicle-to-grid, and space heating load control for residential houses with on-site photovoltaics (PV). A case study is conducted on 1–10 net zero energy houses with detailed empirical data, resulting in 8–33% yearly electricity cost savings per household with various electric vehicle and space heating system combinations. The self-consumption of PV is also significantly increased. Additional benefits through increasing the number of cooperating households are minor and saturate already at around 3–5 households. Permitting electricity transfer between the houses and EV charging stations at workplaces increases self-sufficiency significantly, but it provides limited economic benefit. The additional cost savings from vehicle-to-grid compared to smart charging are minor due to increased battery degradation, despite a significant self-sufficiency increase. If the optimization is conducted without taking the battery degradation cost into account, the added monetary value of vehicle-to-grid can even be negative due to the unmanaged degradation. Neglecting battery degradation completely leads to overestimation of the vehicle-to-grid cost benefit. <s> BIB036 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Optimal operation of hybrid energy systems is a big challenge in power systems. Nowadays, in addition to the optimum performance of energy systems, their pollution issue has been a hot topic between researchers. In this paper, a multi-objective model is proposed for economic and environmental operation of a battery/fuel cell/photovoltaic (PV) hybrid energy system in the presence of demand response program (DRP). In the proposed paper, the first objective function is minimization of total cost of hybrid energy system. The second objective function is minimization of total CO 2 emission which is in conflict with the first objective function. So, a multi-objective optimization model is presented to model the hybrid system’s optimal and environmental performance problem with considering DRP. The proposed multi-objective model is solved by e-constraint method and then fuzzy satisfying technique is employed to select the best possible solution. Also, positive effects of DRP on the economic and environmental performance of hybrid system are analyzed. A mixed-integer linear program is used to simulate the proposed model and the obtained results are compared with weighted sum approach to show the effectiveness of proposed method. <s> BIB037 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> This paper proposes real-time Energy Management System (EMS) for a residential hybrid ac/dc microgrid. The residential microgrid is organized in two different distribution systems. A dc distribution bus which interconnect the renewable energy sources (RES), energy storage systems (ESS) and the building's common facilities; while the apartments are supplied by an ac distribution system connected to the grid. This architecture avoids any modifications in the electrical installation that supplies energy to the apartments. A pure dc voltage supply is not yet a feasible approach for residential buildings. This architecture increases the overall efficiency of the distribution by interconnecting the RES and ESS thorough a dc distribution bus, and therefore avoiding unnecessary dc/ac conversion stages. The real-time EMS performs an 24 hours ahead optimization in order to schedule the charge/discharge of the ESS, and the energy injection/consumption from the grid. The EMS estimates the RES generation based on the weather forecasting, together with stochastic consumption modelling of the building. The EMS architecture and the residential microgrid have been implemented and tested in a laboratory scale setup. The results shown how the operational costs of the system are effectively decreased by 28%, even with non-accurate estimation of the RES generation or building parameters. <s> BIB038 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Abstract Today, as a consequence of the growing installation of efficient technologies, such as combined heat and power (CHP) as a co-generation, the integration of electricity through grid supply, Photovoltaic (PV) and energy storage systems as an integrated network is attracting a lot of attention in smart grid applications. To model the interaction among electricity and natural gas, the energy hub framework is adopted to determine a modelling procedure for such multi-carrier energy systems. This paper presents a residential energy hub model for a smart home as a modified framework of conventional energy hubs in a smart grid with consideration of heat pump water heater, coordination of sources and carbon emissions. Therefore, this study is twofold; the first part optimizes the operation of the combined CHP, Photovoltaic and storage system under dynamic pricing. Since residential load management plays a key role in realizing household demand response programs in a smart grid, performing optimal load management in the proposed residential energy hub model is also studied in this paper. To achieve this, the optimization problem is extended by considering modelling of a heat pump water heater. It is also found out that CO 2 signal could give customers an environmental motivation to shift or reduce loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. <s> BIB039 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> In this paper, an optimal probabilistic scheduling model of energy hubs operations is presented. The scheduling of energy hub determines the energy carriers to be purchased as input and converted or stored, in order to meet the energy requests, while minimizing the total hub's cost. However, as many other engineering endeavors, future operating criteria of energy hubs could not be forecasted with certainty. Load and price uncertainties are the most unclear parameters that hub operators deal with. In this regard, this paper proposes a 2 $m$ + 1 point estimation probabilistic scheduling scheme for energy hubs with multiple energy inputs and outputs. One of the applicable tools of energy hubs to have an efficient participation in the liberalized power market with volatile prices is demand response programs (DRPs). While there is plenty of experience in investigating the effect of DRP, it is electricity DRP that receives increasing attention by research and industry. Therefore, the proposed DRP investigates the effect of both responsive thermal and electric loads in reducing the total cost and participation of different facilities in supplying multiple loads. The proposed model envisages the most technical constraints of converters and storages. Several test systems have been investigated in order to confirm the effectiveness of the proposed model. The results verify the capability of the proposed model in covering the energy hub time-varying output demands as well as the economic advantages of implementing the suggested strategy. In addition, the results are compared with 2 $m$ point estimate method and Monte Carlo simulation. <s> BIB040 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <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> BIB041 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> Due to the 2012 change in the renewable energy act the feed-in tariffs, and therefore the number of newly installed photovoltaic systems decreased dramatically in Germany. Particularly in the residential sector as the biggest market new business ideas for photovoltaic systems were developed. Hence a photovoltaic and combined heat-and-power system, which provides not only electricity but also heat. This complex system requires flexible control strategies. A new combined control algorithm is proposed that in contrast to the standard strategies can operate even under incorrect weather and load forecasts without creating discomfort. Furthermore is it applicable for cloud-based solutions. In this paper a high-level model predictive control based on a mixed integer linear programming problem is combined with an additional low-level, rule-based controller. The low-level control compares the set-points of the optimization with the actual values and corrects the set-points according to each system component until the next optimization takes place. The results show that a hybrid system can be successfully controlled by a combined control approach. In case of a cloud-based application the need for an optimization can be reduced by a factor of four without diminishing comfort or facing much higher operational costs. It has also been shown that the combined control algorithm can be used as an energy management of microgrids. <s> BIB042 </s> Energy management for user’s thermal and power needs: A survey <s> Fuel price <s> There is increasing interest in multi-energy communities, which could become important sources of demand response flexibility, especially when equipped with storage. Their location on distribution networks mean their exploitation to solve local capacity congestions may be particularly valuable, whilst their ability to partake in energy/reserve markets can improve their business cases. However, maximizing this flexibility potential by providing multiple services that are subject to uncertain calls is a challenging modeling task. To address this, we present a stochastic energy/reserve mixed integer linear program for a community energy system with consideration of local network constraints. By covering all the relevant energy vectors, the multi-energy formulation allows comprehensive modeling of different flexibility options, namely, multi-energy storage, energy vector substitution, end-service curtailment, and power factor manipulation. A key feature of the approach is its robustness against any reserve call, ensuring that occupant thermal comfort cannot be degraded beyond agreed limits in the event of a call. The approach is demonstrated through a case study that illustrates how the different flexibility options can be used to integrate more electric heat pumps into a capacity constrained smart district that is managed as a community energy system, while maximizing its revenues from multiple markets/services. <s> BIB043
The majority of studies consider a constant fuel price, irrespective of the type of fuel, such as natural gas BIB001 BIB008 BIB009 BIB018 BIB007 BIB010 BIB011 BIB028 BIB040  BIB019 BIB002 BIB012 BIB003 BIB029   BIB043 BIB030 BIB004 BIB041 BIB031 BIB032 BIB042 BIB033 BIB005 BIB020  BIB021 BIB022 BIB023 BIB013 BIB034 BIB014 BIB015 BIB024 BIB035 BIB025 BIB006 BIB016 , liquefied petroleum gas (LPG) BIB026 , oil BIB027 , or gasoline BIB036 . A two-part tariff for natural gas is applied in BIB037 and  , with a monthly fix fee and a variable one, related with the actual purchases. Hourly gas prices are considered in BIB038 , BIB039 and BIB017 , while daily values are applied in BIB036 , where yearly costs are investigated.