Multi-carrier Energy Systems Research Articles

Control of Multicarrier Energy Systems from Buildings to Networks

Cost, efficiency, and emissions concerns have motivated the application of advanced control techniques to multiple carrier energy systems. Research in energy management and control over the last two decades has shown that significant energy and CO2 emissions reductions can be achieved. Within the last decade, this work has expanded to the domain of interconnected energy systems. The interconnection control of multiple energy carriers, conversion devices, and energy storage provides increased flexibility and energy/CO2 reduction potential. The focus of this article is on outlining the control methods required for these systems over a range of energy consumption and timescales. Dynamic interactions between multicarrier systems occur over timescales ranging from 15 minutes to seasons. The constrained nature of the resulting control problems favors optimization-based approaches.

Genetic sizing optimization of residential multi-carrier energy systems: The aim of energy autarky and its cost

The energy transition process fosters decentralized renewable energy generation and is characterized by an increased effort to achieve energy autarky. In this context, residential-size, photovoltaic-based multi-carrier energy systems using hydrogen as seasonal storage are analyzed as a possible solution to gain energy autarky. A high temporal (15 min) and long-term (10 a) power flow simulation approach is applied to a multi-objective optimization algorithm to minimize costs and grid energy feed-out. Approximate pareto-optimal system configurations are analyzed regarding system sizing, energy autarky, and economic performance for three residential building types and four European locations. Building type and location strongly influence the techno-economic feasibility of approximate pareto-optimal system configurations. Low-Energy-Houses are technically and economically most feasible to reach autarky, while Single-Family and Multi-Family-Houses show available PV energy as main restricting factor, which becomes especially problematic at high latitude locations. Under current economic constraints, no cost-competitive autarky system could be identified. Low Energy Houses show the lowest additional cost of 172% compared to a Base system at the low-seasonality location, but cost-competitiveness is possible under certain cost projections within a time horizon of 2030–2035. The developed energy system model is open-source and can be used for future research in this context.

Ten questions concerning energy flexibility in buildings

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings. • Energy flexibility characterization methodologies are needed at the aggregated level. • There is a trend toward decentralized and distributed architectures to harness energy flexibility. • Buildings with/within multicarrier energy systems offer higher levels of flexibility. • Multidisciplinary approaches are needed to address various aspects of the topic. • Supportive policies are key to enable opportunities and incentivize stakeholders.

Risk-based optimal decision-making strategy of a Power-to-Gas integrated energy-hub for exploitation arbitrage in day-ahead electricity and Natural Gas markets

Utilizing multi-carrier energy systems is a significant step toward a lower-carbon economy and affordable energy production. Hence, this paper proposes a novel and comprehensive stochastic-based decision-making strategy for the optimal short-term management of a power-to-gas included energy hub considering the possibility of simultaneous participation as a prosumer in isolated day-ahead natural gas and electricity markets. The proposed model uses cross-product arbitrage between natural gas and electricity markets to submit the optimal bids, including electricity and natural gas buying/selling bids and offers. Furthermore, the uncertainties for day-ahead electricity and natural gas prices have also been taken into account using a scenario generation method. Appropriate risk measurement as conditional value at risk (CVaR) is also integrated into the model to mitigate the risk of expected cost due to market price volatilities. The risk-averse condition has also been considered. The proposed problem is finally formulated as mixed-integer linear programming and solved by executing CPLEX solver in GAMS optimization software. The simulation results demonstrate that implementing power-to-gas equipment and exploiting cross-product arbitrage in the day-ahead Natural gas and electricity markets increase decision-making capability and save up to 16% in the energy procurement cost of an energy hub.

Operation of energy hubs with storage systems, solar, wind and biomass units connected to demand response aggregators

• Day-ahead scheduling has been carried out for energy hub. • Uncertainties of demands, PV, solar heat, wind and electricity prices are considered. • Demand response aggregator has been used as an input source of electricity. • Demand response effect on EH operation cost has been investigated. • Effect of storage systems on hub operation costs have been evaluated. Energy Hubs (EHs) play an important role in sustainable cities; they are multi-carrier energy systems that can satisfy different energy needs of consumers by relying on the conversion and storage of energy sources as well as renewable energy sources. With efficient and reliable energy supply, EHs may significantly contribute in developments of sustainable cities. In this paper, day-ahead scheduling of EHs is done, while they are connected to demand response aggregators. The studied EH includes photovoltaic and wind renewable sources, biomass, hydrogen electrolyzer, combined heat and power unit, solar heater, boiler, electric, thermal and hydrogen storage systems. Besides electric grid and gas network as input sources, EH may purchase electricity from demand response aggregators. Information gap decision theory (IGDT) is employed as a risk-aware method to handle uncertainties of electric, thermal and hydrogen demands, photovoltaic and wind power, solar heat and electricity prices. The scheduling is carried out from the perspective of the uncertainty free, risk-averse and risk- seeking decision-makers. The problem is formulated as a mixed-integer model and is solved using CPLEX solver in General algebraic modeling system (GAMS). The impact of risk awareness and deviation factors of critical and target costs on day-ahead scheduling and EH operation costs is investigated. The results show that the transaction with demand response aggregator decreases EH operation cost by 20.1%. The results also show that electric, thermal and hydron storage systems respectively decrease the operation cost by 3, 1.7 and 2.1%.

Impact of CO2 reduction targets on the integrated planning of municipal multi-energy carrier systems with grid infrastructures and energy conversion units

National climate protection goals require the emission reduction in municipal energy supply systems. Those systems include energy conversion units like heatings, energy storages, and grids for electricity, gas, and heat. This paper presents a mixed-integer linear optimization model for the integrated and multi-modal planning of such municipal energy systems to reach a defined CO2 emission limit with the least-cost system design. The exemplary application of the model to a study case with 3900 buildings shows that the flexible hybrid energy system resulting without emission restrictions gradually transforms into an inflexible system solely relying on pellet heaters and photovoltaic units when tightening the emission limit by just a few percentage points.

Integrated optimization of multi-carrier energy systems: Water-energy nexus case

This paper focuses on the optimal operation of integrated electrical and water energy networks at the distribution level considering the demand response program. In this manner, the water network with various components including reservoirs, tanks, fixed and variable speed pumps, and control valves is modeled. Newton-Raphson method is adopted to solve the water flow problem. This technique, unlike existing methods, can be applied to any network with any type of topology, whether radial, circular or hybrid. The electrical network is also modeled with the relevant constraints and coupled with the water network to supply the required electrical power. The optimization problem in a practical energy system, including a standard IEEE 33-bus electrical network, and the North Marin water network has been solved by a teaching learning-based optimization algorithm as a parameter-free method. The objective is to minimize the total operation cost. With the participation of the water network in a demand response program, the optimal charging and discharging of tanks, and pumps scheduling has been carried out, which consequently has led to operation cost reduction. In addition, numerical results have proved that the utilization of variable speed pumps can reduce about 7% of the total costs.

Solving the Integrated Optimal Power and Gas Flow Problem by Improved Crow Search Algorithm

This paper solves the integrated optimal power and gas flow (IOPGF) problem in a multicarrier energy system (MES), to find the operating variables of electrical and natural gas networks satisfying some practical constraints. The proposed problem models optimal power flow (OPF), optimal gas flow (OGF), and economic heat dispatch (EHD) equations which make it a very complex optimization problem that cannot be easily solved by the classical optimization techniques. In addition, a population-based evolutionary algorithm (PEA), namely the improved crow search algorithm (ICSA) is introduced and applied to solve the OPF and the IOPGF problems. The results are compared with other well-known PEAs to ensure the higher performance of the ICSA. Also, the superior performance of the ICSA is confirmed by applying it to solve five well-known benchmark functions (BFs).

Development of a dual digital signal processor based cyber-physical system model for a multi-carrier energy system

Multi-energy systems are under exploration for a variety of benefits that can provide multi-energy demands with combined electricity, heat and gas, the most widespread energy demands, to end-users. To satisfy such requirements, this paper integrates a multi-carrier energy system (MCES), including electricity, heat and hydrogen with the cyber-physical system (CPS) model to establish a novel energy CPS. Firstly, the finite state machine (FSM) and the time series method are used to establish a unified CPS architecture of energy nodes. Simultaneously, the embedded architecture of the dual digital signal processor (DSP) system at the energy node level is proposed to implement external control and internal self-control for the energy CPS. Secondly, the CPS modeling method in MCES is conducted by analyzing multi-class energy nodes including energy source nodes, energy conversion nodes, energy storage nodes and energy end-user nodes. Subsequently, the physical system, information system and control system of a reversible solid oxide fuel cell (RSOFC) node and the dynamic operation of MCES are analyzed and simulated by introducing the CPS model, where the controllability and interactivity of the designed energy CPS model are further demonstrated.

Optimal resilient operation of hub energy system considering uncertain parameters

The high potential of hub energy in power systems has considerable profits to improve resiliency and sustainability of system. Variable energy sources embedded in multi-carrier energy systems could be used to propose a robust operation model for improve resiliency of multi-carrier systems during high risk time intervals such as extreme weather condition. Thermal energy sector could be rescheduled for storing heat generated by combined heat and power (CHP) units and boilers in certain periods to coordinate with electrical sector when required. Such cooperation increase operator’s capability to manage electrical grid during time intervals with high uncertainty of outages due to extreme weather condition. Accordingly, in this paper an information gap decision theory (IGDT) method is used to model uncertainty of power outages in optimal system operation. By applying the IGDT method, distribution network operator is able to employ an appropriate methodology to have a robust optimal operation. Also, such methodology leads to make suitable decisions during high risk time intervals such as extreme weather condition. The simulation results proved that proposed model improves the performance and resilience of system by cooperation between thermal and electrical sectors. Also, the chance of electrical grid for successful islanding process increased by reduction in power imbalance during high risk time intervals.

Mixed-Integer Linear Programming for Decentralized Multi-Carrier Optimal Energy Management of a Micro-Grid

Increasing the load demand and penetration of renewable energy sources (RESs) poses real challenges for optimal energy management of distribution networks. Moreover, considering multi-carrier energy systems has increased the efficiency of systems, and provides an opportunity for using the advantages of RESs. In this regard, we adopted a new framework based on the new challenges in the multi-carrier energy micro-grid (MEMG). In the proposed method, a comprehensive MEMG was modeled that benefits from a large assortment of distributed energy resources (DERs), such as micro-turbines, fuel cells, wind turbines, and energy storage. Considering many DERs is necessary, because these resources could cover one another’s disadvantages, which have a great impact on the total cost of the MEMG and decrease the emission impacts of fossil-fuel-based units. Furthermore, waste power plants, inverters, rectifiers, and emission constraints are considered in the proposed method for modeling a practical MEMG. Additionally, for modeling the uncertainty of stochastic parameters, a model based on a multilayer neural network was used in this paper. The results of this study indicate that using a decentralized model, along with stochastic methods for predicting uncertainty, can reduce operational costs in micro-grids and computational complexity compared with optimal centralized programming methods. Finally, the equations and results obtained from the proposed method were evaluated by experiments.

A two-point estimate approach for energy management of multi-carrier energy systems incorporating demand response programs

Gas-based power plants have attracted more attention in providing electrical energy worldwide because of their lower costs and air pollution. In addition, the use of multi-carrier energy systems has several advantages, such as sustainability benefits and improving performance in supplying the energy demand. This study aims to optimize the total operation cost of multi-carrier energy systems considering the uncertain parameters. The storage technology and consumption side assist the operator in achieving lower costs based on conceptions of demand response programs. Therefore, this study presents a comprehensive mathematical model for the coordinated operation of integrated multi-carrier energy systems while the operational constraints of both gas and power networks are considered. Furthermore, this paper utilizes a new uncertainty modeling method based on Hong's two-point estimate method for addressing the uncertainties of load consumption and wind generation. The proposed model is applied to a gas and power multi-carrier energy system through four case studies. The results affirm the high performance of the presented method and investigate the influence of demand response programs in both sides of energy carriers.

Information gap-based scheduling strategy of a multi-energy retailer with integrated demand response program

• Modeling a multi-energy retailer in the framework of multi-carrier energy systems. • Developing a multi-energy retailer based on gas/cooling/heating/power carriers. • Presenting an IGDT method to cope with uncertainties in the optimization model. • Integrating flexible options, e.g. hybrid energy storage in the optimization model. • Proposing integrated DR to control multiple energy demands simultaneously. Multi-energy systems (MESs) were introduced to enhance the flexibility and efficiency of conventional energy distribution systems. In this new trend, it is possible to supply different energy carriers simultaneously by the multi-energy retailer (MER), under which, without the need to transfer all of them to different locations, the loads are fed centrally, and the customers can purchase all the carriers they need from the desired MER. Motivated by these descriptions, this paper focuses on the optimal scheduling of MER in the integrated energy system, including natural gas, electricity, cooling, and heating, under the information gap decision theory (IGDT) framework. The MER's goal is to maximize profits by considering the uncertainty of the day-ahead power market. The proposed IGDT approach without a probabilistic distribution function analyzes the risk-aversion strategy associated with electricity price uncertainty. In this paper, integrated demand response (IDR) for natural gas, electrical, heating, and cooling loads are developed simultaneously as a flexible demand-side management resource. The proposed model contains multiple technologies owned by MER, including hybrid energy storage technology, combined heat and power, chiller, boiler units, and renewable resources. Numerical results from different cases illustrate the effectiveness of IDR installation in terms of profitability. Besides, the MER can serve multiple demands more efficiently. Under the IGDT approach, the purchasing power cost is reduced up to 24.23%.

A modified rule-based energy management scheme for optimal operation of a hybrid PV-wind-Stirling engine integrated multi-carrier energy system

In this study, rule-based energy management strategies (EMS) based on the modifications of the traditional load following (LF) and circuit charging (CC) have been proposed and developed to effectively coordinate the operation of an integrated multi-carrier hybrid energy system. The proposed EMS aim to overcome some of the challenges of the traditional rule-based EMS and broaden their application to the management of complex energy systems. The study deploys a bi-level optimisation scheme to obtain the optimal number of system components that simultaneously minimises the cost, reliability and emissions, in the outer-loop and implements the rule-based EMS in the inner-loop. Also, the results of the optimal system have been simulated for a 48 h timespan, to investigate the effects of the proposed EMS on the Stirling back-up start-ups, battery storage limits, and generation of other energy vectors. The results indicate the deployment of split back-up and batteries minimise the commitment of the back-up, dumped power and emissions. However, the number of start-ups of the back-up increases appreciably by 15.34% and 36%, with the deployment of 2-split and 4-split Stirling, respectively in CC with battery storage. Correspondingly, the operational cost of the system rises as the number of splits increases, but only a slight change in the energy cost is observed, because of the significant reductions in the capacity of the green generators. Interestingly, the batteries record many duty cycles, store less energy and attain lower discharge limits as many small capacity ST back-ups are deployed. Other results demonstrate the additional capabilities of the proposed EMS in handling complex energy systems by the substantial increase in the generation of heating and cooling with increasing splits of the back-up and inclusion of batteries in the optimal system.

A max–min–max robust optimization model for multi-carrier energy systems integrated with power to gas storage system

The volatile nature of the electricity market prices and renewable resources imposes remarkable challenges for multi-energy systems operators to make the appropriate decisions. Therefore, this paper offers a linear max–min–max robust optimization-based decision-making tool that incorporates both uncertainties of the electricity market price and the wind generation. Besides interaction with the electricity market, the EH purchases natural gas to feed the combined heat and power (CHP) and boiler units and supply gas demands. An electrical storage system is also used to smooth the unfavorable volatility nature of the electricity market price. Besides, an uncertainty budget model is proposed to consider both negative and positive deviation of electricity market prices, which gives the capability to increase the robustness of the system against the error of forecasting uncertainty sources. The nonlinearities arisen from the model are linearized using effective approaches and the resulted linear mathematical model is solved by GAMS. Moreover, the power to gas (P2G) storage system is integrated with the EH in order to create a link between the electrical and natural gas networks by converting the electricity to hydrogen and then to natural gas. Simulation results demonstrate that using P2G saves 6.9% in gas purchase cost and 2.13% in total cost.

Optimizing the operation of energy storage embedded energy hub concerning the resilience index of critical load

Dealing with high-impact events in energy hubs requires increasing resistance and restoration capability in multi-carrier energy systems. Assessing resilience as an essential and high priority measure must be done to ensure the optimal and economical operation of the energy hub. Thus, this paper introduces a cost-based optimization model of the energy hub that contains storage and considers resilience constraints and other general constraints. In this methodology, loads are categorized as critical and non-critical. The approach defines the highest priority for supplying the critical loads, whereas interruptions in non-critical loads must be minimized. That in this context, storages play a vital role. Four scenarios are investigated as normal situations and contingency cases. The contingency scenarios examine energy carrier interruptions such as electricity, gas, and heat input in an energy hub system. The mathematical model of this problem is founded based on a Mixed Integer Nonlinear Programming (MINLP), which DICOPT solver implements in GAMS. Mentioned model is evaluated by simulation of different case studies for the given energy hub test system. The trade-off between increasing critical load resiliency and reducing total costs is investigated. The results show that as critical load flexibility increases, total costs increase. In the worst-case scenario, only up to 13% of the total load can also be considered as critical load. Moreover, in different scenarios, the results demonstrate that using storage devices helps to increase resiliency Furthermore, in severe events, using storage devices has an increasing trend.

Optimal design and techno-economic analysis of renewable-based multi-carrier energy systems for industries: A case study of a food factory in China

This paper investigates the techno-economic characteristics of renewable-based energy system design options that need to meet the multi-vector energy demand, i.e. electricity, heat and hydrogen of a food factory in four different places and two different years in China. A two-stage optimization approach is proposed: Firstly, the Hybrid Optimization Model for Electric Renewable (HOMER) software is used to obtain all feasible scenarios meeting demands with the lowest cost; Then, a Multi-Criteria Decision-Making method, Vise Kriterijumska Optimizacija kompromisno Resenje (VIKOR), is adopted to re-evaluate all feasible scenarios to determine the optimal solution considering not only the economy but also energy and environment. Different technologies, like power generation: photovoltaic panel (PV), wind turbine (WT) and both together (PV/WT), energy storage: battery, fuel cell (FC), and both (battery/FC), and hydrogen production: steam methane reforming and water electrolysis, are evaluated to find the best combination. The software results show, PV/WT combination usually has advantages over PV alone and WT alone in power generation in locations with abundant solar energy and wind energy. So is the energy storage. Reformer scenarios are much cheaper than electrolyzers. However, the VIKOR results show, the top scenarios in different locations and years are all using electrolyzers to produce hydrogen.

Sizing Optimization of Residential Multi-Carrier Energy Systems: The Aim of Energy Autarky and its Cost

Sizing Optimization of Residential Multi-Carrier Energy Systems: The Aim of Energy Autarky and its Cost

An Optimal Energy Hub Management Integrated EVs and RES Based on Three-Stage Model Considering Various Uncertainties

In order to make significant progress in the operation of power systems and minimizing cost and air pollution, multi-carrier energy systems based on the economic and emission problem have been proposed by implementing various solutions and using different sources. In this study, an energy hub (EH) system includes a wind turbine (WT), photovoltaic (PV), and EVs that are exchanged energy with energy and reserve market considering energy, thermal, and gas demand response program is proposed. But these units due to uncertain behavior create a big problem in balancing between the demand and generated energy. Thus, the uncertainty of WT, PV, load, and electricity market price are modeled with Mont-Carlo method. Besides that, all parameters of the EVs with uncertainty behavior are modeled using a new method that classified EVs based on the capacity of the battery and other features. This method is employed a stochastic optimization approach to simplify the uncertainty modeling for increasing the system reliability. Hence, two objective functions, namely economic cost, and environmental cost are considered. Finally, a three-step strategy is introduced to solve the multi-objective problem as a single objective function. Finally, EH management performance is investigated by implementing the proposed method and elements.

Enabling Technologies for Sector Coupling: A Review on the Role of Heat Pumps and Thermal Energy Storage

In order to reduce greenhouse gas emissions, current and future energy systems need to be made more efficient and sustainable. This change can be accomplished by increasing the penetration of renewable energy sources and using efficient technologies in energy generation systems. One way to improve the operation of the whole energy system is through the generation and end-use sector coupling. Power-to-heat energy conversion and storage technologies, in this view, are enabling technologies that can help in balancing and improving the efficiency of both thermal and electric grids. In the present paper, a comprehensive analysis of the role of heat pumps and thermal energy storage for sector coupling is presented. The main features of the analyzed technologies are presented in the context of smart electric grid, district heating and cooling and multi-carrier energy systems, and recent findings and developments are highlighted. Finally, the technical, social, and economic challenges in the adoption of investigated technologies are discussed.