Multi-carrier Energy Networks Research Articles

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.

An integrated model for citizen energy communities and renewable energy communities based on clean energy package: A two-stage risk-based approach

Energy communities are at the heart of forming sustainable cities. Beside their important role in decarbonization, they can bring economic, and social benefits to the End-users. Based on the Clean Energy Package (CEP) definition in European Union, an energy community is categorized as: Citizen Energy Communities (CECs) and Renewable Energy Communities (RECs). In this regard, modeling an energy management system framework in a Smart Multi-Carrier Energy Network (SMCEN) associated with CECs and RECs is much of interest. In this paper, a two-stage risk-based optimization model is proposed to handle an energy management structure incorporating energy communities. To this purpose, Energy Community Managers (ECMs) of CECs and RECs concurrently solve the problem at the first stage and send the output data to the SMCEN operator. In the second stage, the SMCEN operator solves an optimization problem to maximize its profit considering the SMCEN's constraints. The uncertainties of energy demands as well as on-site generation are modeled by a scenario-based approach, while a risk-modeling technique is employed to handle the SMCEN risk attitudes in multi-carrier energy scheduling. The results verify that the risk attitude of the SMCEN operator is an influential factor to determine the profit of the SMCEN.

An innovative transactive energy architecture for community microgrids in modern multi-carrier energy networks: a Chicago case study

As the technology of multi-energy carbon-free systems is strikingly developed, renewable-based multi-vector energy integration has become a prevalent trend in the decarbonization procedure of multi-carrier energy networks (MCENs). This paper proposes a fair transactive energy model for structuring an innovative local multi-energy trading market to allow multi-carrier multi-microgrids (MCMGs) with 100% renewable energy sources (RESs) in Chicago for free energy exchange aiming to balance energy in the renewable-dominant environment. Indeed, the main goal of the proposed model is to facilitate the modernization of future MCENs that are targeted to be equipped with 100% RESs and require a holistic model engaged with innovative technologies for the realization. To this end, the transactive energy architecture is designed for techno-environmental-economic assessing hybrid MCMGs to increase their flexibility in unbroken energy serving, decreasing their dependency on the main grid, and improving their economic benefits by considering their contribution level in energy interactions. To effectively model uncertainties of MCENs with 100% RESs, the novel hybrid technique is proposed that considers various stochastic changes of uncertain parameters to achieve confident results. The results highlighted the capability of the proposed model in effectively utilizing fully produced clean energy as well as continuously multi-energy serving of MCMGs in the presence of 100% RESs. Moreover, MCMGs reached techno-environmental-economic benefits by operating under the proposed transactive energy-based model, in which the technical, environmental, and economic goals are respectively realized by considering all constraints of MCENs, producing 100% clean energy by RESs, and reducing the total energy cost from $1,274,742.55 in the based model to $1,159,235.89 in the proposed one.

A comprehensive optimal power and gas flow in multi-carrier energy networks in the presence of energy storage systems considering demand response programs

• Presenting a complete model of an EH contains RES to investigate the OPGF problem. • Modeling the operation limits of CHP, and ramp rate of generators. • Considering demand response programs for both electrical and thermal loads. • Analyzing several EHs, allocated in various buses to minimize the operation costs. • Applying the GAMS software to modified integrated power and gas IEEE14-bus system. Energy hubs (EHs) can help connect different types of energy and create a multi-carrier energy network (MCEN), thereby promoting energy network operation flexibility. Due to the connection of different types of energy in these networks, reduction of operating costs has become a controversial challenge. Herein, the optimal power and gas flow (OPGF) problem in an MCEN was investigated in the presence of wind and solar power sources, electrical and thermal energy storage (EES and TES), and electrical and thermal demand response programs (EDRP and TDRP). In the mentioned EHs, the operating costs of electric and gas networks were minimized, and the operation of EHs was simultaneously investigated. All constraints of the electric and gas networks and different equipment, including combined heat and power (CHP) units, were also considered. The simulation results in GAMS showed that the simultaneous optimization of the operation cost of the case study network, including 14 electric buses and 9 gas buses, during a 24-hour period was reduced up to 4%. Also, due to the lower price of gas, its consumption has increased by about 5%, and this ultimately decreases the total cost of optimal energy distribution of the network compared to the case in which the mentioned strategies and programs were not implemented.

Optimal operation of the energy hubs in the islanded multi-carrier energy system using Cournot model

Growing the electrical and heat demand and increasing the need for the reliable and the cost-effective energy distribution systems facilitate using the multi-carrier energy distribution systems to supply demand. A multi-carrier energy network is a system that supports multiple types of energy carriers such as electricity, natural gas, and heat. This paper presents a new island-mode operation method for multi-carrier distribution systems where in case of the occurrence of a fault or critical event, the local network is disconnected from the main grid. This local network consisting of three energy hubs where the aim of these hubs is to supply their local energy demand with the minimum cost. For this purpose, the energy hubs decide to optimal scheduling of their resources and the optimal trading energy with other ones. To model such decision making framework, the Cournot competition model is employed in which the price of energy exchange among energy hubs is modeled based on the Time-Of-Use demand response program. This model is mathematically solved using Karush–Kuhn–Tucker conditions. For this purpose, these conditions are applied on the optimization problem of all hubs, simultaneously. To show the effectiveness of the model, it is applied on a test case with three hubs. The results show the optimal behavior of the hubs in such framework to minimize their operation cost.

A graph-based model framework for steady-state load flow problems of general multi-carrier energy systems

Coupling single-carrier networks into multi-carrier energy systems (MESs) has recently become more important. Conventional load flow models for the separate single-carrier networks are not able to capture the full extend of the coupling. Recently, different models for multi-carrier energy networks have been proposed, either using the energy hub (EH) concept, or using a case specific approach. Although the EH concept can be applied to a general MES, it is unclear how the EH should be represented in the graph of the MES. On the other hand, the case specific approaches are not easily applicable to general MESs. This paper presents a graph-based framework for steady-state load flow analysis of general MESs. Furthermore, the effect of coupling on the resulting integrated system of equations is investigated. The proposed framework is validated using a small MES. This example shows that our framework is applicable to a general MES, and that it generalizes both the EH concept and the case specific approach.

A bi-level model for optimal bidding of a multi-carrier technical virtual power plant in energy markets

Distributed energy resources (DERs) play an important role in the future vision of distribution energy networks. This paper with the aim of promoting DERs role in energy markets proposes a new framework for the optimal bidding strategy of a Technical Virtual Power Plant (TVPP) in different markets. In the proposed framework, it is assumed that the TVPP energy power is distributed throughout a multi-carrier energy network (MCEN) which consists of district heat networks (DHNs) and a distributed electricity network (DEN). In the MCEN, CHP units are the linkage between DHNs and DEN. With the goal of maximizing the profit of TVPP in different local and wholesale energy markets, a bi-level optimization model is formularized to set scheduling of DERs. As the upper level, the TVPP sets the schedule of its DERs considering technical constraints of DHNs and DENs. In the lower level, however, the TVPP runs an optimization problem to estimate the energy price in wholesale energy markets considering the forecasted bids of other rivals. The proposed model is implemented on a test system and the results confirms that the local market creates an opportunity for CHP owner, TVPP and the heat consumers to make more profit as well as achieving better technical performance for distribution energy systems.

Optimal Operation of Multi-Carrier Energy Networks Considering Uncertain Parameters and Thermal Energy Storage

The coordination of energy carriers in energy systems has significant benefits in enhancing the flexibility, efficiency, and sustainability characteristics of energy networks. These benefits are of great importance for multi-carrier energy networks due to the complexity of obtaining optimal dispatch, considering the non-convex nature of their energy conversion. The current study proposes a robust operation model for the coordination of multi-carrier systems, including electricity, gas, heat, and water carriers concerning thermal energy storage technology. Thermal energy storage is for storing extra heat generated by combined heat and power (CHP) plants and boilers in time intervals with low heat demand on the system and discharging it when required. Energy network operators should have the capability to manage uncertain energy loads to study the impact of load variation on the decision-making process in network operation. Accordingly, this study employs an information gap decision theory (IGDT) method to model the uncertainty of the power demand in optimal system operation. By applying the IGDT approach, the operator of the energy system can use the appropriate methodology to obtain a robust optimal operation. Such a modeling approach helps the operator to make suitable decisions about probable variations in power load. The introduced model is applied in a test system for evaluating the performance and effectiveness of the introduced scheme.

Optimal operation of multi-carrier energy networks with gas, power, heating, and water energy sources considering different energy storage technologies

Abstract The investigation of interdependency among energy carriers in different energy networks is beneficial for network operators in minimizing the operation cost of the whole networks and supplying the energy demands continuously. This study aims at evaluating the effectiveness of considering interconnections among energy sources, which is known as multi-carrier energy systems, on the operation of the energy networks. The proposed scheme aims to analyze the gas, power, heating, and water energy sources and their interdependencies in operation of multi-carrier energy networks considering the gas and power networks constraints and interdependent constraints of all the energy carriers. Accordingly, the role of combined heat and power technology, gas-fired power plants, pumped-storage systems, gas storage, heat buffer tank, and wind turbines as well as the power and gas network constraints are studied in the current work. The operation of multi-carrier energy systems in the presence of the above-mentioned technologies are studied to meet daily power, gas, heating and water loads. The proposed scheme is implemented in a case study to evaluate the practicality and performance of the scheme, which is analyzed by providing two cases for the selected multi-carrier energy network.

Optimal energy flow in integrated energy distribution systems considering unbalanced operation of power distribution systems

This paper focuses on the operation optimization of the integrated energy distribution systems, including electrical, natural gas, and district heating distribution systems. According to the unbalanced nature of the electrical distribution networks, a comprehensive model of the unbalanced electrical distribution network is established. A non-iterative three-phase load flow method, based on holomorphic-embedding, is proposed and implemented for solving the load flow of the unbalanced radial distribution electrical network. The energy flow of natural gas and heat networks are modeled and solved by a graph theory-based method. Teaching-learning based optimization algorithm is implemented to solve the optimization problem, considering various constraints of the proposed integrated energy distribution system. The objective is minimizing the total operation cost of energy flow in multi-carrier energy networks. The performance of the proposed method is validated in an integrated energy distribution system, including a 19-bus unbalanced radial electrical network, a typical 20-node natural gas, and a 30-node district heating network. The results verify the effectiveness of the proposed integrated energy system operation and applicability of the proposed optimal energy flow.

Co‐planning of electricity and gas networks considering risk level assessment

This study provides a risk-based gas and electricity expansion planning model to coordinate the expansion of electricity and gas networks in a multi-carrier energy network. Generally, electricity and gas networks have separate owners having no mechanisms to share information. In this study, a distributed algorithm based on alternative direction method of multipliers is developed to preserve the privacy of electricity and gas networks while maintaining a coordination link. Probabilistic outage of components is implemented into the expansion planning model to investigate the interactions between electricity and gas networks and evaluate the risk of contingencies in generating units, transmission lines, and pipelines. Second fuel of gas consuming generating units is modelled to have a holistic approach while studying electricity and gas interactions in the case of contingencies. Moreover, conditional value at risk is used to adjust a balance between risk and investment where each of energy parties can decide on the risk level of their expansion plans. The proposed expansion planning approach is applied to a realistic case study to evaluate its performance.

Capacity Planning of Energy Hub in Multi-Carrier Energy Networks: A Data-Driven Robust Stochastic Programming Approach

Cascaded utilization of natural gas, electric power, and heat could leverage synergetic effects among these energy resources, precipitating the advent of integrated energy systems. In such infrastructures, energy hub is an interface among different energy systems, playing the role of energy production, conversion, and storage. The capacity of energy hub largely determines how tightly these energy systems are coupled and how flexibly the whole system would behave. This paper proposes a data-driven two-stage robust stochastic programming model for energy hub capacity planning with distributional robustness guarantee. Renewable generation and load uncertainties are modelled by a family of ambiguous probability distributions near an empirical distribution in the sense of Kullback–Leibler (KL) divergence measure. The objective is to minimize the sum of the construction cost and the expected life-cycle operating cost under the worst-case distribution restricted in the ambiguity set. Network energy flow in normal operating conditions is considered; demand supply reliability in extreme conditions is taken into account via robust chance constraints. Through duality theory and sampling average approximation, the proposed model is transformed into an equivalent convex program with a nonlinear objective and linear constraints, and is solved by an outer-approximation algorithm that entails solving only linear program. Case studies demonstrate the effectiveness of the proposed model and method.

Maestro: A Python library for multi-carrier energy district optimal control design

This paper introduces the Maestro library. This library for Python focuses on the design of predictive controllers for small to medium-scale energy networks. It allows non-expert users to describe multi-carrier (electricity, heat, gas) energy networks with a range of energy production, conversion, and storage component classes; together with consumption patterns. Based on this description a predictive controller can be synthesized and tested in simulation. This controller manages the dispatch of energy in the network, making sure that the demands are met while minimizing the total energy cost. Alternative objectives can be specified. The library uses a mixed-integer linear modeling framework to describe the network and can be used in stand-alone based on standardized input files or as part of the larger energy network control platform PENTAGON.

Planning, operation and flexibility contribution of multi‐carrier energy storage systems in integrated energy systems

Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day-ahead scheduling for multi-carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi-carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi-period optimal energy flow problem in multi-carrier energy networks, this study utilises the well-known teaching-learning based optimisation algorithm. The investigated multi-carrier energy system consists of electrical, natural gas and district heating sub-networks in which an ESS is included in the electrical sub-network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively.

Optimal dispatch of large multi-carrier energy networks considering energy conversion functions

An integrated coordination of multi-carrier energy networks including gas, heating, cooling and electricity can increase the flexibility, efficiency and sustainability of energy systems. The optimal dispatch of such systems is complicated by the non-convex nature of their energy conversion processes. Although these processes can be represented in mixed-integer linear programmes, real-time constraints of an online dispatcher may not be satisfied. In this paper, two approaches for alleviating this problem are developed and compared: one is based on a relaxed mixed-integer linear formulation and the other on mathematical optimization with complementarity constraints. Simulation results on realistic systems demonstrate that both approaches solve large multi-carrier dispatch problems efficiently. The mathematical optimization with complementarity constraints is computationally less intensive but the relaxed mixed-integer linear formulation is numerically more robust.

Optimal place, size, and operation of combined heat and power in multi carrier energy networks considering network reliability, power loss, and voltage profile

Distributed energy resources play a substantial role in today's electrical distribution networks. Combined heat and power (CHP) is a great example of the technologies with potential of combining different energy infrastructures such as gas and electricity. In this study, optimal planning (placing and sizing) of CHPs at arbitrary nodes within a system containing an electrical and natural gas network is considered. Energy hub (EH) approach is employed in order to calculate the energy consumption (operation) of each bus along with its relevant costs. CHP is optimally planned (placed and sized) at the network by considering the operation costs, power loss, network reliability, and voltage penalty. Simulation is carried out on a 33 bus radial distribution system containing electrical and gas networks. The network is integrated by wind and energy storage in some electrical buses. Genetic algorithm of Matlab and Cplex solver of GAMS are used to solve the optimisation problem. The results show that optimal planning of the CHPs reduces total costs, operation costs, network reliability, power loss, and voltage penalty by 9.4, 10.8, 15.4, 16.8, and 10.1%, respectively. The results also show operation of the formed EHs at the arbitrary nodes with optimal planning of the CHPs.

Demand Response Programs in Optimal Operation of Multi-Carrier Energy Networks

Smart grid worldwide project provides integration of Distributed Energy Resources (DERs) to electrical power systems, especially electrical distribution networks. Combined Heat and Power (CHP) and Demand Response (DR) programs are considered as prominent resources of DERs. CHP allows integration of different energy infrastructures and DR enables customers to participate in energy market. In this paper, effect of DR programs is evaluated on multi-carrier energy networks based on the Energy Hub (EH) approach. Time-based and incentive-based programs of DR are taken into account. GAMS software is used to solve Mixed Integer Linear Programming (MILP) models of the proposed EH. Results demonstrate that different DR programs have different effects on load shape and hub operation costs. The results confirm that load participation factors by customers provide more operation cost reductions for EH owners. The results also demonstrate possible purchasing distributions of energy carriers from the networks for operation cost minimization in the presence of DR programs. In addition, the results show, in order to operation costs reduction, which technology should be optimally operated when different DR programs are applied on the multi-carrier network.

Simultaneous integrated optimal energy flow of electricity, gas, and heat

In this paper, an integrated approach to optimize electrical, natural gas, and district heating networks simultaneously is studied. Several interdependencies between these infrastructures are considered in details including a nonlinear part-load performance for boilers and CHPs besides the valve-point effect for generators. A novel approach based on selecting an appropriate set of state-variables for the problem is proposed that eliminates the addition of any new variable to convert irregular equations into a regular set while the optimization problem is still solvable. As a large optimization problem, the optimal solution cannot be achieved by conventional mathematical techniques. Hence, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching–learning based optimization algorithm is utilized to solve the multi-period optimal power flow problem of multi-carrier energy networks. The proposed scheme is implemented and applied to a typical multi-carrier energy network. Results are compared with some other conventional heuristic algorithms and the applicability and superiority of the proposed methodology is verified.

Multi-objective operation management of a multi-carrier energy system

In this paper, a multi-objective optimization approach for multi-carrier energy networks is discussed. A multi-carrier energy network is a system consists of various types of energy carrier such as electricity, natural gas, and heat. Minimizing the total cost of operation of such a system is a typical objective for optimization while another important objective is to minimize the total emission generated by the whole network. It is shown in the paper that the cost and emission functions are two opposite objectives that decreasing one of them would increase the other one and vice versa. Therefore, a multi-objective optimization should be utilized to obtain the global optima of the problem based on the priority of each objective. According to the large size of the problem in actual networks, this could be a non-linear, non-convex, non-smooth, and high-dimension optimization problem that mathematical techniques could be trapped in local minima. Hence, it is better to use evolutionary techniques instead. To do so, a fuzzy decision making method is proposed in this paper which is merged with the well-known modified teaching-learning based optimization algorithm. This approach is implemented and applied to a typical multi-carrier energy network to verify the proposed methodology.