Multi-carrier Energy Research Articles

Assessing battery degradation as a key performance indicator for multi-objective optimization of multi-carrier energy systems

The Pareto frontier is extensively adopted in multi-objective optimization, especially in multi-carrier energy system modeling. Despite the various methodologies available to derive the frontier, it represents different optimal solutions, making the final selection non-trivial. The modeler’s expertise is crucial in determining the weight factors assigned to each objective for selecting the final solution from the Pareto frontier. This study proposes a novel approach to support such decision-making, introducing an additional key performance indicator, the state of health of the battery, evaluated through physical battery modeling. By comparing different scheduling schemes in multi-objective multi-carrier energy systems, each with its distinct battery operational strategy, this newly introduced indicator has deployed to automatically identify the ultimate solution from the Pareto frontier, without additional weighting coefficients. Such an approach, therefore, automates the decision process, which supports easy engineering, especially for the small scale multi-energy systems such as smart homes, like the case study presented in this work that has four distinct energy carriers, adopting the 12 V 128 Ah LFP chemistry Li-ion battery modules, demonstrates the effectiveness of this automated selection process. Furthermore, when compared to the maximum values across the entire frontier, the automatically chosen solution exhibits reductions of 27.96% in CO2 emissions and 3.67% reduction in overall costs. Over long-term operation, this approach has the potential to extend battery lifespan by up to 26.67%, directly impacting the economics of multi-carrier energy systems.

Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

This paper introduces a novel model for optimizing microgrid systems by integrating multi-purpose renewable energy (MEM) and cutting-edge technologies, including electric vehicles (EVs). The proposed MEM demand response programs encompass various energy sources such as wind energy, multi-carrier energy storage technologies, boilers, combined heat and cooling units, EVs, P2G (power-to-gas), and demand response capabilities. The primary objective is to minimize the total operational cost of the microgrid system. A distinctive aspect of the proposed method lies in considering the prices of all energy carriers as unknown variables. Market prices are integrated into the modeling process, incorporating scenarios with reasonable probabilities and taking into account demand-side management programs. Moreover, the model allows for customizable programming of different parts of the multi-energy microgrids, with a focus on maintaining convexity principles for the operating area of each CHP (combined heat and power) unit. To tackle the unique complexity of this optimization problem, a developed blue whale optimization algorithm is proposed. This algorithm builds upon the main whale optimization algorithm (WOA), a promising population-based optimization approach. However, the effectiveness of WOA heavily relies on the careful setting of exploration and exploitation parameters, which may lead to being trapped in local optima. To address this challenge, the paper introduces a self-adaptation modification based on wavelet theory to enhance the WOA's performance. The proposed model and optimization method are thoroughly evaluated through simulation studies for different scenarios, showcasing their efficacy in achieving cost-efficient and sustainable microgrid operation. The combination of MEM and emerging technologies like EVs, along with the improved optimization algorithm, marks a significant contribution to advancing the planning and operation of microgrid systems towards low-cost, reliable, and environmentally-friendly energy solutions.

Customer-oriented scheduling of active distribution system considering integrated demand response programs and multi-carrier energy hubs

The increasing development of infrastructure and improvement of automation services have changed the role of household loads from fixed and unresponsive loads to responsive household loads. Meanwhile, the demand response program capability of smart residential consumers, considering their comfort level along with their power exchange effects on energy hubs, is an important part of active distribution system Scheduling, which should be noted comprehensively. In this regard, this paper presents a two-stage optimization framework based on iteration for customer-oriented scheduling of an active distribution system by incorporating the participation of smart residential consumers in energy hubs. In the first stage-first level, a flexible integrated demand response program process along with a storage system for smart residential consumers with real-time electricity prices is implemented to maximize the comfort level of smart residential consumers. In the first stage-second level, the energy hub operator presents day-ahead optimal self-scheduling to maximize their profit and send their bid strategies to the distribution system operator, considering the effects of smart residential consumer's power exchange on profit and cost. Finally, in the second stage of the optimization model, customer-oriented scheduling of an active distribution system is implemented by the distribution system operator to minimize operating costs. The comfort level of smart residential consumers for 20, 25, and 30 households of the first, second, and third energy hubs are increased by 112.92%, 161.64%, and 120.77%, respectively, compared to the base case. In addition, the total operating costs of active distribution system and energy hubs are reduced by $38,200, $4643, $3287, and $5523, respectively, compared to the base case.

Financial Analysis for a Multi-Carrier Energy System Equipped with CCHP

The interest on distributed generation has been increasing in recent years, especially due to technical development on generation systems that meet environmental and energy policy concerns. One of the most important distributed energy technologies is Combined Cooling, Heat and Power (CCHP) systems. CCHP is a small and self-contained electric, heating and cooling generation plant that can provide power for household applications, commercial or industrial facilities. It can reduce power loss and enhance service reliability in distribution systems. Since the input of a CCHP system is natural gas then natural gas price is so important in CCHP penetration level in DG’s market. In this paper authors by considering gas price, capital cost and also sales revenue, find the effects of these parameters in financial parameters for a multi-carrier energy system with optimal size and operation by applying COMFAR III software.

Impact of Flexibility Implementation on the Control of a Solar District Heating System

Renewable energy sources, distributed generation, multi-energy carriers, distributed storage, and low-temperature district heating systems, among others, are demanding a change in the way thermal networks are conceived, understood, and operated. Governments around the world are moving to increase the renewable share in energy distribution networks through legislation like the European Directive 2012/27 in Europe, and solar energy integration into district heating systems is arising as an interesting option to reduce operation costs and carbon footprint. This conveys an important investment that adds complexity to the management of thermal networks and often delays the return on investment due to the unpredictability of renewable energy sources, like solar radiation. To this end, this paper presents an optimisation methodology to aid in the operative control of an existing solar district heating system located in the northwest of France. The modelling of the system, which includes a large-scale solar field, a biomass boiler, a gas boiler, and thermal energy storage, was previously built in Dymola. The optimisation of this network was performed using MATLAB’s genetic algorithm (GA) and running the Dymola model as functional mock-up units, FMUs, using Simulink’s FMI Kit. The results show that the methodology presented here can reduce the current operation costs and improve the use of the daily storage of the DH system by a combination of mass flow control and the implementation of a flexibility function for the end-users. The cost-per-kWh was reduced by as much as 16% in a single day, and the share of heat supplied by the solar field on this day was increased by 5.22%.

Security Issues and Challenges for IoT-based Smart Multi Energy Carrier Systems

Security Issues and Challenges for IoT-based Smart Multi Energy Carrier Systems

Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response

This work proposes a sequential stochastic coordinated energy management scheme (SCEMS) for a multi-energy carrier zero bus microgrid (ZBMG) in the presence of distributed tri-generation plants (TGPs), renewable sources, energy storage systems (ESSs), plug-in hybrid electric vehicles (PHEVs), auxiliary boiler and chiller units, and controllable loads. The first stage of the proposed architecture deals with implementing an integrated price-based demand response program to maximize consumers’ benefits and boost demand-side participation. The impacts of grid-to-vehicle and vehicle-to-grid modes of PHEV operation are investigated in this study. The second stage of this formulated problem considers the optimal power allocation of TGPs, ESS devices and auxiliary units aiming to satisfy economic, environmental, flexible, and reliability-driven objectives. Furthermore, to ensure optimal serving of heat and cooling load, novel heat and cool-following strategies are suggested. The uncertainties associated with the renewable generators, PHEV load, grid energy price, and load demands are modelled and incorporated in the SCEMS following “Hong’s 2m+1 point estimate method”. This stage also optimizes the distribution network structure for efficient microgrid operation. Moreover, a novel P−PQVδ−zero+ bus triplet approach of controlling power factor of dispatchable distributed generator is proposed. The efficacy of the framed problem is corroborated through examples. The simulation studies show that the proposed coordinated energy management strategy can: (1) economically accomplish peak shaving and valley filling for multi-energy demands; (2) reduce operation cost and electric energy loss by 8.19%∼10.38% and 41.63%∼47.91%, respectively; (3) enhance average flexibility index by 4.06%∼5.62% but with a marginal rise in emission by 0.39%∼1.68%; and (4) improve node voltage profile of the system.

Hydrogen for harvesting the potential of offshore wind: A North Sea case study

Economical offshore wind developments depend on alternatives for cost-efficient transmission of the generated energy to connecting markets. Distance to shore, availability of an offshore power grid and scale of the wind farm may impede export through power cables. Conversion to H2 through offshore electrolysis may for certain offshore wind assets be a future option to enable energy export. Here, we analyse the cost sensitivity of offshore electrolysis for harvesting offshore wind in the North Sea using a technology-detailed multi-carrier energy system modelling framework for analysis of energy export. We include multiple investment options for electric power and hydrogen export including HVDC cables, new hydrogen pipelines, tie-in to existing pipelines and pipelines with linepacking. Existing hydropower is included in the modelling, and the effect on offshore electrolysis from increased pumping capacity in the hydropower system is analysed. Considering the lack of empirical cost data on offshore electrolysis, as well as the high uncertainty in future electricity and H2 prices, we analyse the cost sensitivity of offshore electrolysis in the North Sea by comparing costs relative to onshore electrolysis and energy prices relative to a nominal scenario. Offshore electrolysis is shown to be particularly sensitive to the electricity price, and an electricity price of 1.5 times the baseline assumption was needed to provide sufficient offshore energy for any significant offshore electrolysis investments. On the other hand, too high electricity prices would have a negative impact on offshore electrolysis because the energy is more valuable as electricity, even at the cost of increased wind power curtailment. This shows that there is a window-of-opportunity in terms of onshore electricity where offshore electrolysis can play a significant role in the production of H2. Pumped hydropower increases the maximum installed offshore electrolysis at the optimal electricity and H2 prices and makes offshore electrolysis more competitive at low electricity prices. Linepacking can make offshore electrolysis investments more robust against low H2 and high electricity prices as it allow for more variable H2 production through storing excess energy from offshore. The increased electrolysis capacity needed for variable electrolyser operation and linepacking is installed onshore due to its lower CAPEX compared to offshore installations.

Technical and economic analysis of a multicarrier building energy hub concept with heating loads at different temperature levels

Integration of renewable and waste energy sources, decentralisation, electrification and energy storage, are key pillars of energy transition in the district heating sector. Such a strategy can be implemented at the building level using the multi-carrier energy hub concept, which has recently gained popularity. This paper presents a technical and financial feasibility study of a building-integrated multi-carrier energy hub concept. The case study is performed for an existing large medical centre, where demand occurs simultaneously for electricity, cold, and heat at different temperature levels, including hot process water and steam. The central idea is to replace the high-exergy steam with heat from the municipal district heating network. The concept also includes the integration of heating and cooling loads, heat storage and implementation of an on-site photovoltaic (PV) power plant. The key components of the proposed hub technological structure are high-temperature heat pumps. Five alternative structures of the system configuration are examined using an hour-by-hour annual simulation. The results reveal that the proposed multi-carrier energy hub is fully operational. The project is also profitable, which results mainly from significant energy savings compared to the existing heat supply system using high-pressure steam. However, the profitability strongly depends on the system configuration, and the choice of the best option is ambiguous. The system with heat storage reaches the highest net present value (NPV) while the one without the storage results in the highest internal rate of return (IRR) due to significantly lower capital expenditures (CAPEX). In all cases, the project leads to a considerable cumulative CO2 emissions reduction, which is at the level of 1351 up to 2961 tons annually depending on system configuration.

Optimal operation of coordinated multi-carrier energy hubs for integrated electricity and gas networks

"In the field of energy supply, coordinated multi-carrier systems, which include natural gas and electric energy, offer unique opportunities to enhance energy efficiency and flexibility. Nevertheless, the interdependence between electricity and natural gas networks poses various challenges related to the flow of electricity and gas in feeders, pipes, and connection points. To address these challenges, the concept of an energy hub emerges as an essential component in multi-carrier energy systems, serving as a connection point between the electricity grid and gas grids. This study introduces an optimal operation method for coordinating gas and electricity distribution networks through interconnected energy hubs. The infrastructure of the proposed energy hub comprises a combined heat and power unit, a boiler, an electric energy storage system, a heat pump, and a gas-burning unit, effectively meeting heating and electrical load requirements. The model formulation follows a stochastic approach based on a two-stage scenario. Considering uncertainties related to wind energy, electric load, and real-time energy prices, the primary objective of this study is to minimize the total operating cost of the energy system. The optimization problem is solved using Wall's optimization algorithm. To meet its energy requirements, the integrated energy system presented in this paper actively participates in the real and daily electric energy markets, as well as the natural gas market, providing the necessary energy resources. Additionally, the model incorporates realistic descriptions of electric power and gas flow in feeders and gas pipelines, considering AC load distribution and the Weymouth equation. With consideration of connectivity constraints, the proposed model offers a comprehensive representation of the integrated electricity and gas networks. The simulations conducted on the integrated energy system, comprising a 33-bus electric network and a 6-node gas network with interconnected energy hubs, highlight a remarkable 12 % decrease in electricity purchase costs and a substantial 18 % reduction in natural gas procurement expenses, underscoring the cost-efficiency and effectiveness of the stochastic planning approach.

A cooperative resilience-oriented planning framework for integrated distribution energy systems and multi-carrier energy microgrids considering energy trading

Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.

A cooperative transactive multi-carrier energy control mechanism with P2P energy + reserve trading using Nash bargaining game theory under renewables uncertainty

Transactive Energy Control (TEC) as a market-based control is a critical notion for scheduling Multi-Carrier Energy Systems (MCESs) in local networks and forming an Energy Hub (EH). Nevertheless, implementing TEC for scheduling and controlling MCESs is extremely difficult due to the lack of a cooperative TEC model that accounts for network constraints and the uncertainty of Renewable Energy Sources (RESs). This paper defines and formulates Prosumer-Based Multi-Carrier Energy Systems (PB-MCESs), which include electricity, heat, cooling, and gas hubs to enable internal coordination of resources and flexibility extraction for PB-MCESs. Subsequently, Nash Bargaining Game Theory is employed to construct a cooperative TEC that prioritizes P2P energy trade. In addition to P2P energy trading, PB-MCESs can trade their reserve in a P2P fashion to mitigate their uncertainty. PB-MCESs estimate the level of uncertainty using stochastic programming and allot a reserve capacity based on this estimation in order to manage their uncertainty via P2P reserve trading and internal reserves. PB-MCES can also control their risk by altering their risk-taking factor in accordance with the Conditional Value-at-Risk (CVAR) index. Implementations have demonstrated that the proposed cooperative TEC decreases total costs by 17.14% and that the proposed P2P reserve trading reduces total costs by 16.32%.

A novel approach for multi-stage investment decisions and dynamic variations in medium-term energy planning for multi-energy carriers community

So far, energy planning methods faced several challenges in achieving a proper assessment due to the extreme variability of input conditions. To considerably decrease the computational efforts in achieving solutions, most of the analyses reported in the scientific literature assume constant costs and demand over the entire planning horizon in a typical year. However, results might not be perfectly aligned with real ones because of the incapability of some models to address several aspects like energy demand/production variability, energy and technology costs, efficiency degradation, and use of different energy carriers. This paper proposes a novel methodology that optimises both the long-term planning and short-term scheduling decisions in the management of a multi-energy carries community by means of a Mixed Integer Linear Programming model that also considers the modular design of technologies (e.g., technological devices selection from a discrete set of variants). Such a model has been applied to the case study of a University campus in Italy, whose historical demand data were used for its energy planning with a time horizon of 30 years. Three scenarios have been analysed: (i) the Business As Usual, (ii) the Sector-coupling scenario, and, finally, (iii) the Hydrogen deployment one. The results are obtained under different energy scenarios, showing the effectiveness of the methodology in dealing with multi-investment stages at different planning levels in a reasonable computational time. In particular, they showed that deploying more sustainable technologies would increase the cost of the electricity (between 43%–89%), while reducing other energy carriers’ cost (about 60%) and lowering all energy carriers’ carbon footprint (between 50%–80%). From a long-term perspective, (i) the use of sector-coupling technologies is beneficial from both economic and environmental points of view, and ii) dynamic variations of some parameters can strongly affect the deployment of high-cost technologies to be installed beyond 2030.

Co-optimisation of power-gas networks connected to industrial energy hubs with high penetration of storage systems

With proliferation of gas-fired generators and increase in the number of interconnected power-gas networks, co-optimisation of power-gas nexus has become a hot research area. Energy hubs (EHs) are efficient and reliable multi-carrier energy systems which typically take in electricity and natural gas and supply different forms of energy demands. As EHs typically import electricity and gas, their connection into a power-gas nexus may be an interesting option; their electricity input may be connected into an electric bus and their gas input may be connected into a gas node. This research focuses on the operation of power-gas nexus connected to industrial EHs. The Belgian network connected to a 24-bus power system has been used as case study and the studied EH includes boiler, combined heat and power unit, battery and thermal storage and supplies electricity and heat demands. The changes in shadow prices at different electric buses and gas nodes as well as congestion in both networks are investigated. The effect of connection points of EH on total operation cost, electric system cost and gas network cost is investigated and the optimal connection points are determined. According to the results, about 49.4 % of total cost belongs to electric system and the remaining 50.6 % belongs to gas system and a large portion of electric system cost belongs to demand shed cost. According to the results, the electric system is severely congested. At some buses and some hours, due to demand shed, the shadow price of electricity soars 10,000$/MWh which equals value of lost electric load.

Application of Unsupervised Learning in Implementation of Joint Power and Index Modulation Access in V2X Systems

The implementation of joint Power-and-Index Modulation Access (PIMA), which is an effective way of realizing non-orthogonal multiple access (NOMA), requires a process of user ordering over the available subcarriers, before multiplexing their data streams for power and index modulation. The conventional way of user ordering in NOMA with respect to the channel state information can be impractical and expensive given the large numbers of users and subcarriers, in Vehicle-to-Everything (V2X) communications. Therefore, this paper proposes a novel approach that leverages K-Means clustering to learn the pattern of multi-user multi-carrier energy variations and use them to effectively order the users. For this, we develop several learning algorithms and an analytical model for the Symbol Error Probability (SEP) of PIMA, which is used to quickly generate part of the training dataset (transfer learning). The results of the comprehensive evaluations that have been carried out in this study show that the proposed approach outperforms the benchmark techniques in terms of SEP. It is also shown that the proposed scheme provides improved performance in terms of SEP as compared to distance-based ordering in realistic V2X scenarios using synthetic mobility traces for the locations of vehicles along multi-lane highways.

Robust Integrated Energy Management of a Smart Home Considering Discomfort Degree-Day

This article implements an integrated electrical and thermal energy management of a smart home in real time, taking into account the residents' comfort conditions. The smart home can benefit from a multi-carrier energy system. The energy resources include electrical network, photovoltaic generating unit and storage unit, as well as a hybrid water heating system supplied from solar irradiation and natural gas network. Additionally, electrical and thermal loads are categorized into three types: i)time flexible, ii) power flexible and iii) fixed loads. Taking advantage of smart grid technology, an energy management system receives required economic, technical, and climatic information in real-time, and decides the energy schedules time by time. The integrated energy management problem is formulated to optimize economic and discomfort metrics. A novel metric of discomfort degree-day is proposed to measure the residents' dissatisfaction with the indoor temperature, which considers both magnitude and duration of deviation from a desired temperature in a day. Additionally, the uncertainties in solar irradiation and outdoor temperature are presented by uncertainty sets, and handled through a robust optimization model. The out-of-sample results through a realistic case study indicate that a more robust schedule of energy resources and loads (e.g., ranging from 20% to 100% robustness) is obtained with negligible change (e.g., < 1%) in the operation cost, but at the expense of 48.82% and 58.97% increment of dissatisfaction under the uncertainty sets with the confidence levels of 0.6 and 0.8, respectively.

Two-timescale autonomous energy management strategy based on multi-agent deep reinforcement learning approach for residential multicarrier energy system

In the residential multicarrier energy system (RMES), autonomous energy management (AEM) is beneficial for prosumers' costs that actively controls generation, energy conversion, and storage in real-time. Conventional model-based AEM methods rely on forecast models of distributed energy resources and proper system parameters, which are difficult for practical application. This article proposes a novel model-free two-timescale real-time AEM strategy for the RMES. The energy management problem in the RMES is time-decomposed into two timescales (hourly-ahead external energy trading and 15-min ahead internal energy conversion), formulated as Markov Games. Then, defining a multi-agent system not only enables separate energy management diagrams but also accelerates learning. Intelligent agents account for optimizing energy trading and energy conversion to minimize daily costs under the training of a deep deterministic policy gradient algorithm. In order to learn a collaborative strategy, all agents are trained centrally while being executed based on local information in a decentralized manner for fast response. A deterministic study indicates that the proposed AEM strategy can effectively and flexibly schedule the operation of components with respect to different price signals and load profiles. In the stochastic study that considers the error thresholds of solar panels generation and loads, the difference between the energy cost of test scenarios with a trained strategy and the no-regret learning method is equal to 0.32%, which is appropriate. In addition, the proposed method not only achieves a reduction in power imbalance but also exhibits lower energy costs when compared to various benchmark methods.

Reliability-Oriented Planning Framework for Smart Cities: From Interconnected Micro Energy Hubs to Macro Energy Hub Scale

Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

Multi-objective design of the energy storage-based combined heat and power off-grid system to supply of thermal and electricity consumption energies

Simultaneous consumption of electrical and thermal energy management, together with economic and technical advantages of close-to-consumption feeding of consumers, has demonstrated the importance of multi-carrier energy systems planning in recent years. Therefore, this paper presents the fuzzy decision-based multi-objective planning of an islanded hybrid system (IHS) with renewable energy sources (RESs), combined heat and power system (CHP), boiler, electric and thermal energy storage systems (ESSs) to provide a clean energy for electric and thermal consumers. Scheme is responsible for minimizing total weighted annual operating costs and pollution consumption of system. It is subjected to planning-operation constraints of sources and storages based on the electrical and thermal energy management. To obtain optimal solution with a low standard deviation error, a hybrid algorithm based on ant-lion optimizer and krill herd optimization is developed. Novelties include extraction of IHS with electric and thermal sources and ESSs, provision of clean energy to electrical and thermal consumers, extraction of scheme based on hybrid algorithm. By implementing scheme on data of Rafsanjan city in Iran and investigating numerical results, ability of design to obtain optimal hybrid combination of sources and storages to supply electric and thermal consumers from an economic and environmental point of view is confirmed. So that storage system (especially thermal storage) is able to reduce the planning cost and pollution level about to 1 %–6 % in compared with case including sources to supply thermal and electricity consumption energies. The aforementioned solution algorithm is able to extract the most optimal solution compared to non-combined algorithms and the standard deviation of its response is around 0.96. Fuzzy decision-making has obtained an optimal point for the aforementioned objective functions, which are about 20 % to 23 % away from their minimum value.

Scheduling of Multiple Energy Consumption in The Smart Buildings with Peak Demand Management

The global energy crisis and the depletion of fossil fuels have become pressing concerns, leading experts to search for alternative solutions. This paper presents an analysis of the day-ahead operation of the multi-carrier energy system (MCES) with the aim of minimizing operational costs, reducing pollution emissions, and maximizing consumers' comfort. The authors propose an optimal scheduling strategy called energy demand curtailment (EDCS), which aims at efficiently managing electrical energy consumption. Additionally, they consider an on-site generation strategy (OGS) for consumers to operate their own energy storages. Both EDCS and OGS are modeled based on demand-side management (DSM). To optimize these strategies and achieve their objectives, fuzzy logic is employed as an optimization approach along with objective functions. Finally, two scenarios are examined through numerical simulations to illustrate the effectiveness of this approach in optimizing energy utilization in MCE.