Multi-carrier Energy Research Articles

Coordinated distributed model predictive control for multi energy carrier systems.

Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging. This paper proposes a distributed control system for use with dynamic EHs. The RESs and loads present in the multi-carrier system cause the dynamics considering here. In order to optimise system performance, this research suggests a distributed model predictive control strategy that considers expected behaviour and operational restrictions. The strategy's potential is demonstrated via simulations in which the proposed scheme is applied to a benchmark system.

Optimal operation of multi-energy carriers considering energy hubs in unbalanced distribution networks under uncertainty

This article presents a two-stage stochastic programming model to address the dispatching scheduling problem in an energy hub, considering an unbalanced active low-voltage (LV) network. A three-phase version of the second-order cone relaxation of DistFlow AC optimal power flow (AC-OPF) is employed to incorporate unbalanced network constraints, while the objective minimizes the Local Energy Community (LEC) operational cost. The model results have been validated using OpenDSS, encompassing energy losses, voltage levels, and active/reactive power. Likewise, a comparative analysis between the three-phase model and the traditional single-phase model, using a modified version of the IEEE European LV Test Feeder as a case study, reveals interesting differences, such that the single-phase model underestimates voltage limits during photovoltaic (PV) system operation and overestimates energy purchased from the main grid, compared with the three-phase model. Similarly, the comparison results reveal that discrepancies between the single and three-phase models intensify as the power injected from PV systems rises. This notably impacts the total energy purchased from the grid, battery operation, and the satisfaction of thermal consumption through electricity. Finally, while the three-phase model offers valuable insights into security levels for voltage and grid energy purchase, its longer computational time makes it more suitable for strategic use rather than daily operational frameworks.

Techno-economic synergy analysis of integrated electric power and hydrogen system

This study introduces an integrated renewable-penetrated electric power and hydrogen energy system (IRPHS) framework to address the integration challenges of renewable energy sources (RESs) through the development of a hydrogen-based multi-carrier energy system. The IRPHS combines a renewable-penetrated electric power system (RP-EPS) with hydrogen energy system (HES), integrating urban transportation systems (UTSs), and incorporating short and long-term hydrogen storage. By employing scenario-based stochastic programming and the alternating direction method of multipliers (ADMM), the study tackles uncertainties of renewable power and complexities of IRPHS problem. The research evaluates benefits of integrated versus stand-alone operation, focusing on flexibility across hydrogen generation, transportation, and storage. Findings highlight cost-efficient hydrogen generation, enhanced efficiency, and economic benefits in RES deployment, particularly with long-term hydrogen storage.

Enhancing energy hub efficiency through advanced modelling and optimization techniques: A case study on micro-refinery output products and parking lot integration

A new model of energy carriers (micro-refinery output products) in the concept of an energy hub is presented. In addition, in the presented model, the effect of different models of parking lot in an energy hub is analyzed. In this study, the uncertainty of the number of electric vehicles was modeled using the Monte Carlo method, and then considering the same conditions, the uncertainty of the number of electric vehicles was calculated using the Probability-Possibility hybrid method. In addition, the study uses a scenario-based approach to address uncertainties related to multi-carrier energy demand and multi-carrier energy prices. In the present paper, ensuring water demand is of great importance, which is why the proposed energy hub structure of a sea desalination unit and sour water treatment that is extracted from the micro refinery output was analyzed. The optimization model presented in this paper for the energy hub is a complex integer linear programming (MILP) that is solved using a CPLEX solver in the GAMS environment. The results show that the use of the Probability-Possibility method resulted in an 8 % reduction in the final energy supply cost in the energy hub compared to the Monte Carlo method.

Real-time optimization for integrated R2 charging and refueling stations in a multi-carrier energy system considering hydrogen chain management

This study presents a real-time optimization-based strategy for sustainable energy management using an energy hub that integrates combined heat and power (CHP) units, heat pumps, and fuel-cell technology to meet community electricity, heating, and cooling demands. It incorporates renewable-based R2 stations that leverage photovoltaic and wind power, integrated with hydrogen production and storage, to supply stationary energy consumers and the transportation sector. The system provides hydrogen for fuel cell electric vehicles (FCEVs) and electricity for electric vehicles (EVs). Integrating the R2 station with the primary energy hub optimizes the use of locally generated power, reducing dependence on external sources. Hydrogen fuel is produced on-site and off-site, focusing on solving the capacitated routing problem for efficient delivery. The system's architecture is modeled using a mixed-integer linear programming (MILP) approach. According to the results, no energy is drawn from the grid to meet the end-user's electricity demand.

Economic and risk based optimal energy management of a multi-carrier energy system with water electrolyzing and steam methane reform technologies for hydrogen production

This paper proposes stochastic programming to optimal economic scheduling problems of a sustainable integrated energy system with renewable resources including power, natural gas, and hydrogen carriers. Hydrogen is considered a byproduct of electrical power and natural gas carriers using water electrolysis and steam methane reform process technologies. The main objective is to minimize the operation and emission costs while addressing uncertainties of electric/heating/cooling demands, electricity price, and renewable generation using scenario-based stochastic programming. To achieve a risk-hedging strategy, downside risk constraints are involved to minimize the risky scenarios portfolio. The whole problem is modeled as mixed-integer linear programming in GAMS optimization software. The analysis revealed that the expected operation cost without DRC is $ 577.52 and the expected risk-in-cost value for risky scenarios is $ 470.7. However, a conservative decision for reaching zero risk is costly and increases the expected cost up to $ 2901.1, which is not economically viable. A modest decision-making strategy compromises the cost and risk values where for decreasing the risk-in-cost to $ 288.7 (36.8%), the expected cost increased by to $1010.39 (75.95%). Moreover, the risk-averse decision-maker lowers the power sold to the grid, increases the use of water electrolyzing instead of the SMR process to reduce the environmental charges, and tries to increase flexibility with the help of hydrogen and battery storage.

Transactive energy framework in fuel cell based multi-carrier energy hubs based on conditional value-at-risk

The prevalence of peer-to-peer operation among industrial energy hubs is considered a promoting method to enhance local energy autonomy, efficiency, and flexibility. By conducting stochastic programming, this paper aims to minimize the operating cost of multiple industrial energy hubs that trade power and heat energies with each other and utility. Concerning the important role of hydrogen systems, fuel cell-based combined heat and power (CHP) generators are employed within the hubs to increase sustainability. The presence of electric vehicles (EVs) and shiftable loads enhances the flexibility during peer-to-peer operating mode. The trading price for power and heat energies are derived at first by solving a deterministic problem and considering average values for demands and generation. However, to address the uncertainties in renewable generation, electric and heat demands, and electricity prices under operation, scenario-based stochastic programming tries to optimize the expected objective function. Furthermore, the Conditional Value-at-Risk (CVaR) approach is proposed to provide various risk-averse scheduling tactics and measure the risk of each scenario. The results show that the proposed risk-constrained approach reduces the risk and makes cautious decisions about energy resources and trades. In this regard, the expected profit has been reduced by 75%, i.e., from 1636.78 NOK under a risk-neutral case to 414.97 NOK under a risk-averse case to reduce the expected risk in operating cost.

Optimal operation of multi-carrier energy systems considering demand response: A hybrid scenario-based/IGDT uncertainty method

The optimal operation of multi-carrier energy systems (MCESs) has opened new horizons for energy network management and the satisfaction of consumers. In this paper, the optimization of the MCES's operation cost is considered by combining several energy hubs (EHs). To make optimal use of thermal and electrical demand response programs (TDRPs and EDRPs), the cost factors are extracted using a new sensitivity analysis (SA) method. Then, taking into account the optimized cost coefficients in EDRP and TDRP, the optimal operation of MCES is investigated in the presence of uncertain parameters. In this paper, a two-stage uncertainty method is used for solving the uncertain problem. First, the uncertainty of wind, photovoltaic (PV), and electrical and thermal loads is modeled with a scenario-based method, in the second step, the price uncertainty of generated electricity is added to the previous model using the information gap decision theory (IGDT). A hybrid scenario-based/IGDT uncertainty has been performed simultaneously at the GAMS platform. The results show the impact of each uncertainty parameter and system resilience due to the optimization done in the problem and the EDRP and TDRP programs. The results show about 135$ difference between the lowest and highest scenarios at a total operation cost.

A fuzzy cloud theory-based stochastic model for multi-carrier energy hubs in grid-connected and islanded operations

A fuzzy cloud theory-based stochastic model for multi-carrier energy hubs in grid-connected and islanded operations

Component and system levels limitations in power-hydrogen systems: Analytical review

This study identifies limitations and research and development (R&D) gaps at both the component and system levels for hydrogen energy systems (HESs) and specifies how these limitations impact HES adoption within the electric power system (EPS) decarbonization roadmap. To trace these limitations and potential solutions, an analytical review is conducted in electrification and integration of HESs, renewable energy sources (RESs), and multi-carrier energy systems (MCESs) in sequence. The study also innovatively categorizes HES integration challenges into component and system levels. At the component level, technological aspects of hydrogen generation, storage, transportation, and refueling are explored. At the system level, HES coordination, hydrogen market frameworks, and adoption challenges are evaluated. Findings highlight R&D gaps, including misalignment between HES operational targets and techno-economic development, integration insufficiency, model deficiencies, and challenges in operational complexity. This study provides insights for sustainable energy integration, by supporting the transition to a decarbonized energy system.

Isolated Work of a Multi-Energy Carrier Microgrid

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers’ electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers’ needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system’s operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system’s behavior, a one-week time horizon is considered.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

Empowering sustainable energy communities: Optimizing multi-carrier energy systems with green energy, uncertainty management, and demand response

Energy management has emerged as a pressing concern in the planning and operation of smart cities, driven by the escalating demand for energy. Concurrently, demand response programs (DRPs) have gained significant attention in recent years, owing to their profound impact on the power system. This paper introduces an approach aimed at minimizing operational costs and environmental emissions within a smart city reliant on grid-connected power. A novel green energy scheduling for a multi-carrier energy community is presented to achieve a sustainable development. The proposed method places a premium on maximizing the utilization of renewable energy sources. Recognizing the inherent uncertainty associated with green energy resources, this approach seeks to enhance system reliability by addressing this challenge. This paper's primary objective is to tackle the intricate problem of power management in a smart city while factoring in the uncertainty stemming from green energy resources and the integration of DRPs. To address these complexities, the paper leverages the CPLEX solver embedded in the GAMS software application, while uncertainties in the system are modelled through the application of the Monte-Carlo algorithm. The results show that considering the uncertainty of green energy resources, the total cost of the system can be reduced by $504 by performing the DRPs.

Optimal and distributed energy management in interconnected energy hubs

Recently, multi-carrier energy systems (MCESs) have been rapidly developed as flexible multi-generation systems aiming to satisfy load demands by purchasing, converting, and storing different energy carriers. This study specifically focuses on the optimal and robust large-scale coordination of interconnected energy hubs (IEHs) in an iterative consensus-based procedure considering distribution network losses. Furthermore, a new robust-based hybrid IGDT/consensus algorithm is introduced to achieve risk-averse optimal energy management in IEHs under uncertainty. The fast convergence, needless to collect the total information from all hubs, minimal computational burden, and more robust communication system are the most important features of the proposed distributed consensus algorithm in this study. The effectiveness of the proposed consensus algorithm is verified by simulation results considering various energy trading structures in IEHs at different scales. The obtained results highlight the scalability capability of the proposed method. Regarding an IEHS of 30 energy hubs, the computation burden is lightened by 0.53 (s) and 0.1917 (s), respectively with and without uncertainty. Considering distribution network losses, the total purchasing costs can be increased by 8%. The simulation results also reveal an increase of 11% in the total power trading under the uncertainty.

Risk-driven optimal scheduling of renewable-oriented energy hub under demand response program and energy storages: A novel Entropic value-at-risk modeling

Energy hubs have revolutionized the field of energy management, emerging as state-of-the-art solutions for achieving efficient and sustainable energy systems. These hubs act as integrated platforms that seamlessly combine a variety of energy technologies, facilitating the synergistic interaction of multi-carrier energy systems with diverse energy sources. This paper outlines a framework for an energy hub that integrates a wide range of energy technologies, encompassing electrical, thermal, and cooling systems, alongside renewable resources and storage units. The proposed model incorporates demand response programs (DRPs) to effectively manage energy demand, optimize load distribution, enhance grid stability, and achieve substantial cost reductions. The effectiveness of the proposed framework is demonstrated through careful analysis of four distinct case studies, giving its potential in diverse energy management scenarios. To tackle uncertainties associated with power, heating, and cooling demands, as well as power and gas market prices, wind speed, and solar irradiation, this study utilizes EVaR as a stochastic modeling approach. The EVaR is a novel coherent risk measure incorporated to enhance risk management practices and surpass the limitations of traditional value-at-risk measures. According to the obtained results, due to the conservativeness of the operator, the income has decreased by 23 % while the system operation cost has increased by 36 % in the presence of the EVaR optimization approach.

Modelling and evaluating different multi-carrier energy system configurations for a Dutch house

The urge to reduce the dependence on natural gas for heating at the residential level has led to the deployment of different fossil fuel-free alternatives. In the Netherlands, two technologies are leading the transition: heat pumps, due to their high COP, and photovoltaic–thermal systems, due to their dual electric-thermal output. However, both represent a challenge for users and grid operators, aside from their stochastic behavior. Heat pumps alone can surpass a typical Dutch house’s total energy and power consumption. Photovoltaic–thermal systems, as their only electric homologs, usually have a mismatch between generation and demand, causing energy injections to the grid. From the electric perspective, storage systems are a proven solution to reduce the energy exchange with the distribution network. This paper proposes four multi-carrier energy system configurations for a Dutch household, comprising different combinations of a photovoltaic–thermal system, a battery energy storage, a heat pump, and an underground water tank thermal energy system, providing analytical models for every component (including the thermal losses from the thermal storage to the ground), and the space heating and electrical demands. We determined the components’ compatibility and evaluated the combinations considering their thermal performance, electrical performance, and equivalent CO2 emissions. The results suggest that using a heat pump combined with a photovoltaic system and a battery provides the best trade-off. The photovoltaic–thermal system alone could not supply the thermal demand required for comfortable space heating nor reach temperatures high enough to charge the thermal storage. Combining the thermal storage with the heat pump allows a certain degree of flexibility for the heat pump activation at the cost of COPs between 0.8 and 1.38 when used to charge the thermal storage, thus increasing energy consumption and equivalent emissions considerably.

Optimal operation of multiple energy system considering load management approach and uncertainty

In recent years, the advancement of technology has led to the development of energy networks' infrastructure, resulting in the transformation of conventional networks into smart networks. By incorporating communication equipment, each component of the network can now exchange information with other components, creating an environment where innovative ideas can be implemented. One such idea is the utilization of multi-carrier energy systems, also known as Energy Hubs. Integrating energy hubs into traditional networks used to be a challenging task. However, with the expansion and enhancement of smart networks, the potential for incorporating and locating energy hubs within the power grid has significantly increased. In these systems, the focus shifts from optimizing a single energy carrier to ensuring the optimal operation of a system that encompasses multiple energy carriers, such as natural gas, electric grid, and thermal energy. Energy hubs act as intermediaries between energy supply companies and various consumers, including residential, commercial, and industrial sectors. These energy suppliers can encompass different energy networks, such as electricity, gas, and local heating. The integration of residential and commercial energy hubs has become a crucial aspect of modern energy systems, particularly in the context of load response applications. This paper proposes a novel approach for effectively utilizing residential and commercial energy hubs, taking into account the time of use (ToU) load response program. In the proposed system, the inherent uncertainties of the system are addressed using the two-point estimation method of the model. To achieve the desired objective, the Cuckoo optimization algorithm, a well-established meta-heuristic algorithm for solving optimization problems, is employed. This algorithm aids in determining the optimal scheduling of energy resources while considering ToU tariffs and load management plan signals. The results demonstrate that the proposed approach can significantly reduce energy costs for the energy system.

Efficient operation of combined residential and commercial energy hubs incorporating load management and two-point approximation for uncertainty modeling

In the era of advancing technology, energy networks have evolved into sophisticated smart networks, allowing seamless communication and exchange of information among network components. This transformation opens doors for innovative concepts such as the Multi-Carrier Energy System, or Energy Hub. While integrating energy hubs into traditional networks has posed challenges, the expansion of smart networks has facilitated their adoption within the power grid. Unlike conventional systems that optimize a single energy carrier, energy hubs focus on the optimal operation of a system encompassing multiple energy carriers like natural gas, electric grid, and thermal energy. Serving as intermediaries between energy supplier companies and diverse end-users, these hubs play a pivotal role in the intricate landscape of modern energy systems. This paper introduces a novel strategy for optimizing the utilization of residential and commercial energy hubs, specifically integrating Time-of-Use (ToU) tariffs and Load Response Programs (LRPs). To address inherent uncertainties, the proposed system employs the two-point estimation method within its model. For achieving optimal scheduling of energy resources aligned with ToU tariffs and load management plan signals, the well-established Cuckoo Optimization Algorithm (COA) is applied. The results presented in this study highlight the effectiveness of the proposed approach in significantly reducing energy costs for the energy hub. Notably, these cost reductions are achieved while adhering to the constraints of the ToU Load Management Program (LMP). Furthermore, the paper explores various scenarios to investigate the impact of LRPs on both residential and commercial energy hubs, providing valuable insights into the dynamics of these systems. The numerical outcomes of the study affirm the efficacy of the proposed energy hub system. Implementation of load management plans markedly reduces unsupplied electrical and thermal energy, with scenario 3 showcasing a significant drop from 0.52 to 0.2 in unsupplied electrical energy for the home hub.

Decomposed particle swarm optimization for optimal scheduling in energy hub considering battery lifetime

The energy hub has become pivotal in optimizing multi-carrier energy systems, aiming to enhance the flexibility and efficiency of the overall infrastructure. By incorporating energy storage within these hubs, it becomes feasible to dynamically coordinate the balance between energy generation and consumption. This coordination considers diverse energy pricing structures and local renewable energy generation, leading to substantial reductions in energy expenditures. To further amplify the long-term utilization of batteries, it is essential to account for the associated costs related to their lifespan during the optimization process. Nonetheless, optimizing the operation of an interconnected energy hub system while factoring in battery lifespan introduces a complex challenge, encompassing various limitations, spanning multiple time frames, and involving non-convex aspects. This article introduces an inventive strategy termed the “decomposition technique,” which entails amalgamating particle swarm optimization with a numerical approach. By disassembling the complex optimization predicament into more manageable sub-problems, particularly the organization of storage and other elements within the energy hub system, this approach capitalizes on the individual strengths of particle swarm optimization and the interior point method. To assess the efficacy of this proposed method, it is applied across various scenarios, encompassing a two-hub system and a three-hub system spanning a 24-h timeframe. Through comparison with analytical methods, the results obtained affirm the technique's capability to converge remarkably close to the global minimum. Moreover, the computational efficiency of this approach enables its online implementation, with a reduced time horizon, and ensures real-time optimization. The success of this approach opens up potential applications in diverse energy hub systems and emphasizes its adaptability and scalability. Using the strengths of particle swarm optimization and the interior point method, this technique provides an effective solution for optimizing energy hub systems while considering battery lifetime costs. It enables near-optimal performance while overcoming limitations related to convergence and computation time. The findings of this research contribute to the broader field of energy optimization and pave the way for more efficient and sustainable energy management strategies. The decomposition technique yielded a significant 12% reduction in total energy cost compared to conventional particle swarm optimization. In the three-hub system, the decomposition technique showcased a 15% increase in computational efficiency, affirming its adaptability for larger systems. Considering battery lifetime costs, excluding them led to a 6% boost in energy savings but resulted in a 2% rise in overall system cost, emphasizing the need for a balanced optimization approach.