Multi-carrier Energy Research Articles

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.

A novel energy management framework incorporating multi‐carrier energy hub for smart city

The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi-carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy.

An Optimized and Outage-Resilient Energy Management Framework for Multicarrier Energy Microgrids Integrating Demand Response

Given the frequency of power outages caused by recent crises, including high-impact natural disasters, power and energy supply must be more resilient and cost effective. Owing to this necessity, this article presents a grid-connected multicarrier energy microgrid, which can provide continuous energy for both electrical and heating load demands at a low cost. The model employs a demand response (DR) scheme based on time of use and integrates with the proposed microgrid. Several energy sources—photovoltaics, batteries, a combined heat and power unit, and associated dispatch strategy—are optimized using the deterministic mixed-integer linear programming algorithm. The proposed model is tested in a hospital of California, USA. The results show the benefits of the DR program in terms of techno-economic output when compared to the system without its implementation. Several simulation results also demonstrate the model’s effectiveness and resiliency in maintaining a continuous energy supply in the midst of power outages at various times of the year while lowering costs. Furthermore, a comparative study with the existing methods is performed, which shows the effectiveness of the proposed technique as well.

Matrix modelling and optimisation calculation method for large‐scale integrated We‐Energy

We-Energy (WE), as an important energy unit with full duplex and multi-energy carriers in the integrated energy system, uses the coupling matrix to connect the network-side and demand-side energy. However, the coupling matrix of WE is very hard to be formulated directly due to the complicated internal structure and the flexibility of operation mode. This paper proposes a multi-step method for the modelling of WE. According to the method, the conversion process in the WE can be separated into several steps and the WE model can be built by the coupling matrix in each step. Then, the WE model is extended by considering the renewable energy, location of storage and different types of demand response. Because of the non-linearity caused by the dispatch factors, the computational complexity increases greatly for solving the optimal scheduling issue of the WE. In order to reduce the computational burden, the variable substitution is added in the proposed modelling method. The results of simulation cases are presented to demonstrate the performance of the proposed modelling and calculation method.

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.

Optimal Scheduling of Integrated Demand Response-Enabled Community-Integrated Energy Systems in Uncertain Environments

The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR)-enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and micro-gas turbine (MT), as links of multi-energy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory (SOT) and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. Simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a trade-off between economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the Hybrid Intelligent Algorithm in terms of both optimization results and calculation efficiency.

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.

IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.

Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory (IGDT) method.

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.

Robust management and optimization strategy of energy hub based on uncertainties probability modelling in the presence of demand response programs

The energy hub is considered one of the most important parts of the multi-carrier energy distribution systems. One of the major challenges facing the energy hub idea is the optimal distribution and management of energy based on renewable and non-renewable power generation resources in the presence of demand response programs and uncertainties. This study aims to present an optimal management and distribution model of energy based on uncertainties of probability modelling of renewable energy resources, uncertainties of electrical loads (electric vehicles (EVs)), operating and maintenance cost, cost modelling of greenhouse gas emissions, and electric and thermal demand response programs. Therefore, the proposed model is presented based on a robust optimization model and mixed-integer linear programming. Uncertainties in this research are divided into two categories; technical and economic. The technical part includes the uncertainties caused by renewable energy resources and EVs, simulated based on the Monte Carlo method and their probability distribution function. The economic part includes modelling the uncertainty caused by the price of electricity. The operation model and the objective function of the optimization problem are modelled using the demand response models based on peak shaving and load shifting techniques.

Robust Flexible Unit Commitment in Network-Constrained Multicarrier Energy Systems

The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This article proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multicarrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electricity, gas, and heat consumption patterns and to increase the power dispatch of combined heat and power units. Multienergy storage systems are also included in the proposed model to decrease the impact of multienergy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multicarrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by 2.58% considering the IDR program and multienergy storage systems.

Resilience enhancement of integrated electricity-gas-heating networks through automatic switching in the presence of energy storage systems

Multi-carrier energy systems meet the needs of customers in different forms of energy. Given that these systems cover a large number of customers, it is very important to improve their resilience in emergency conditions. Hence, this paper presents a mixed-integer non-linear programming (MINLP) framework to enhance the resilience of multi-carrier energy systems integrated with electricity-gas-heating networks. In this regard, residential, commercial and industrial energy hubs with different load supply priorities, wind turbines and power to gas (P2G) technologies are considered in the model. In the proposed model, the system operator is capable to enhance system resilience under emergency conditions through automatic switching and implementing the direct load control (DLC) program. In this model, the scenario-based method is utilized to deal with load demand, wind speed, and solar radiation uncertainties. The proposed model is implemented on both medium-scale and large-scale systems and is solved by DECOPT solver in GAMS software. The results indicate that automatic switching reduces the load shedding by 84.17% during the outage of the line. The results also illustrate that the DLC program has reduced load shedding at high priority loads by about 66%. Finally, the high impact of storage systems and P2G technology on enhancing system resilience has been proven.