Optimal Energy Flow Research Articles

Control, regulation and optimization of bidirectional energy flows for electric vehicles’ charging and discharging

Vehicular electrification plays a major role in the reduction of toxic greenhouse gas emissions linked to the transportation sector. However, the differential margin between electricity supply and demand is chaotic; and only a production/consumption balance could reconcile both ends. The adaptation of energy flows is therefore necessary to confront the gigantic energy waste related to electricity storage challenges. Consequently, electric vehicle batteries recently emerged as a solution to the actual limitation of storage capability. Indeed, they are used in this study as means of storage and retrieval of energy.Thus, this paper proposes a control and regulation algorithm aimed at reaching a balanced production/consumption system. The balance is acquired through the bidirectional control of the energy flows related to domestic residences, electric vehicles and the grid. Moreover, a multi-objective optimization of vehicular charging and discharging is assessed using the genetic algorithm to attain an optimal fulfillment of the system’s energetic needs. Once the regulation algorithm is set and the optimizations implemented, the algorithm’s simulation is performed using Matlab. Through the developed algorithm, we aim these major findings: 1. Regulate energy flows depending on supply and demand. 2. Optimize charging and discharging modes. 3. Flows merge towards the system’s equilibrium state.

ADP‐based decentralised algorithm for the optimal energy flow of the electricity–natural gas system

This study proposes a decentralised methodology to deal with the optimal energy flow (OEF) problem of the electricity–natural gas system (EGS). The OEF problem of EGS is formulated as a multi-stage dynamic programming (DP) process, and the approximate DP algorithm is used to decompose it into subproblems. The independent decision of each subproblem can be made by solving Bellman's equation, requiring just a moderate interchange of information among different subproblems. Such that the information privacy and dispatch independency of subsystems (including electricity and natural gas (NG) subsystems) can be ensured. Different from most existing decentralised algorithms, the impact of one subproblem's decision on other subproblems can be estimated in the proposed algorithm, and the parameters of the proposed algorithm are not required for tuning, which makes more sense in real applications. Furthermore, an improved linear cut technique is proposed to handle the non-convexity of the NG network model, so that the tightness of all gas flow constraints can be ensured. Case studies containing small, large and complicated EGSs validate the effectiveness of the proposed approach.

One-segment linearization modeling of electricity-gas system optimization

Linear models of energy systems are preferable in industries because linear optimization problems bring benefits in convergence, efficiency, and convenience for pricing. The nonlinear model of the natural-gas system is a major bottleneck for the fully-linear modeling of electricity-gas systems. In this paper, a one-segment linear gas flow model without the need of integer variables is presented. To facilitate the linear formulation of the gas flow model, a deep learning method is applied to predict the interval of the gas pressure. A special setting of the linearization interval is proposed, which avoids the influence of the range of the linearization interval on the optimal energy flow (OEF) solution. Based on the one-segment linear gas flow model, a fully-linear OEF model of the electricity-gas system is formulated. The efficiency of the proposed method is much improved compared with the traditional mixed integer linear programming approach.

A multi-objective optimization of electric vehicles energy flows: the charging process

As the electrification of vehicles keeps being widespread, and facing the impossibility of storing big amounts of electrical energy, the challenge of controlling and adapting the electricity supply and demand has become a necessity. Therefore, electric vehicles could be an optimal solution for the storage and the retrieval of energy depending on the supply and demand of electricity. Thus, this study proposes an energetic strategy based on a multi-objective and multi-criteria optimization algorithm related to the control of the energy flows between the electric vehicles and the grid, home or building depending on the electricity supply and demand. The main focus at this stage involves the optimization of the vehicles’ charging mode. Hence, in this paper, the multi-objective optimization proposed is first implemented through the presentation of its algorithm and the description and modeling of its objective functions and their corresponding constraints. Then, the genetic algorithm optimization approach is adopted for the charging process of the study to find the optimal solution for each objective function. Besides, the weighted sum approach is applied after the normalization of all the fitness functions and several case studies are carried out to find the optimal solutions based on the priority of specific objectives over the others. Once all calculations are done, a simulation via Matlab software is performed and the results are discussed and compared. The simulation of the results has verified the theoretical calculations proving the effectiveness of the proposed methodology. However, as some of the calculated optimal solutions of the system seem to be conflicting, the prioritization of some objectives over others had to be operated in order to figure out a global solution for the multi-objective system. Thus, as a compromise had to be applied for the calculation of quasi-optimal solutions that would converge towards the Pareto-front, the decision maker’s preference would set the final solution using the weighted sum approach.

System Modeling and Optimal Dispatching of Multi-energy Microgrid with Energy Storage

The coordinated operation and comprehensive utilization of multi-energy sources require systematic research. A multi-energy microgrid (MEMG) is a coupling system with multiple inputs and outputs. In this paper, a system model based on unified energy flows is proposed to describe the static relationship, and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes. Then, the optimal dispatching model of an MEMG is established as a mixed-integer linear programming (MILP) problem using piecewise linear approximation and convex relaxation. Finally, the system model and optimal dispatching method are validated in an MEMG, including district electricity, natural gas and heat supply, and renewable generation. The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.

Interactive Equilibrium of Electricity-Gas Energy Distribution System and Integrated Load Aggregators Considering Energy Pricings: A Master-Slave Approach

Integration of electricity and gas distribution networks improves energy utilization and alleviates environmental pollution. In order to obtain a reasonable energy prices and optimize the day-ahead scheduling scheme under the premise of considering the interests of both supply and demand, an interactive equilibrium model considering electricity-gas energy distribution system and integrated load aggregators is established in this paper. In the upper level, the integrated energy distribution network (IEDN) maximizes the profit via solving the day-ahead optimal energy flow considering energy prices changing in the next day. In the lower level, integrated load aggregators (ILA) minimize charging costs, thus optimizing energy distribution based on integrated demand responses. By using the Karush-Kuhn-Tucker (KKT) optimality conditions and the duality theorem of linear programming, the master-slave game model is transformed into a single layer optimization problem. To solve this problem, second-order cone programming (SOCP) and penalty convex-concave procedure (PCCP) are used to deal with the non-convex functions of electricity-gas distribution system. Therefore, the proposed model is approximated by a mixed-integer second-order cone programming problem. Finally, case studies verify the effectiveness of the proposed model and method.

Mathematical modeling of the technological processes of catering products production

AbstractTechnological processes of catering products manufacture need energy optimization. This article proposes the improvement of mathematical modeling apparatus at the stage of technological processes design and reconstruction. The authors used data from large restaurant‐type catering enterprises typical for million‐plus cities, some of which prepare fast‐food dishes. The possibilities of calorie generation during photosynthesis and the generation of electricity by photocells were examined to understand the energy benefits for catering enterprises. Emphasis is placed on minimizing energy consumption for heating and cooling in food production cycles using cogeneration, regeneration, and generation from local renewable energy sources. Cooking and storage chambers can be differentiated by thermal potentials. The modeling algorithm is deepened by the introduction of links between power consumption and generation equipment. A set of organizational measures of reconstruction is proposed in the design of production systems, which allows one to save up to 33–80% of the total energy costs of a real network of catering outlets.Practical ApplicationsThe use of modern energy technologies in the production processes of public catering enterprises will reduce total costs by up to 80% and improve the overall environmental friendliness of the food industry. While planning reconstruction of basic energy equipment, buildings, and mechanisms, the following must be taken into account. These include concepts of a healthy diet, local renewable energy resources, cultivating local natural food ingredients, and utilizing heat and calorie emissions. Up to 50% of the existing equipment requires such a reconstruction in public catering in developing countries. The principles recommended by the authors for the reconstruction of the production processes of public catering enterprises require the use of advanced algorithms for the mathematical optimization of energy flows and balances.

Optimal Energy Flow for Integrated Energy Systems Considering Gas Transients

This letter presents an optimal energy flow (OEF) for integrated energy systems considering the transient process of natural gas flows, whose main dynamic features are efficiently approximated in our setup. To achieve this, new discretization criteria are proposed to divide the gas flow equations into reasonable temporal and spatial segments to accurately model the gas flow dynamics in a set of difference equations. Simulation results validate the effectiveness of the OEF based on the proposed discretization criteria and its advantages over existing methods.

Hourly electricity and heat Demand Response in the OEF of the integrated electricity‐heat‐natural gas system

Recently, demand-response (DR) programmes are one of the appropriate tools for energy systems to encourage flexible customers to participate in the operation of energy systems. One of the complex tasks in multi-energy environments is optimal energy flow (OEF) problem of these systems. In this regard, this study investigates the OEF of an integrated electrical, heat, and gas system considering flexible heat and electrical demands. The conventional DR programme has been combined with the demand-side energy supplying management activity by introducing switching concept among input energy carriers. The way of the supplying energy of flexible customer can be changed by switching among input energy carriers. Here, the integrated system operator minimises the system operation costs subject to supply flexible consumers’ energy. To solve the complex OEF problem, this study presents a new optimisation algorithm named modified biogeography-based optimisation (BBO) algorithm. In this study, the proposed modification for the original BBO increases the robustness and the capability of the proposed optimisation method. The numerical results show that the flexible DR programme creates smoother energy demand curves in heat and electrical networks and reduce the operating costs of the integrated system.

Power Interchange Analysis for Reliable Vehicle-to-Grid Connectivity

Due to the progressively growing energy demand, electricl vehicles (EVs) are increasingly replacing unfashionable vehicles equipped with internal combustion engines. The new era of modern grid is aiming to unlock the possibility of resource coordination between EVs and power grid. The goal of including vehicle-to-grid (V2G) technology is to enable shared access to power resources. To define the initiative, this article investigates the bidirectional power flow between EVs and the main grid. The article provides a new algorithm framework for energy optimization that enables real-time decision making to facilitate charge/discharge processes in grid connected mode. Accordingly, the energy flow optimization, communications for data exchange, and local controller are joined to support system reliability for both power grid and EV owners at parking lot sites. The local controller is the key component that collects the EV data for decision making through real-time communications with EV platforms. The main responsibility of this controller is managing the energy flow during the process of real-time charging without impacting the basic functionalities of both grid and EV systems. Finally, a case study of a modified IEEE 13-node test feeder is proposed to validate the impact of energy flow optimization using V2G technology. This visionary concept provides improvement in grid scalability and reliability to grid operations through accessing EV power storage as a complementary resource of future energy systems.

Energy optimization and greenhouse gas emissions mitigation for agricultural and horticultural systems in Northern Iran

The objective of this study is to comprehensively optimize energy usage and determine mitigation of greenhouse gas (GHG) emissions in agricultural and horticultural crops of Guilan Province, Iran. For this purpose, required data are collected from eggplant, garlic, tea, hazelnut, kiwifruit and tangerine producers through questionnaires. In this study, GHG emissions are investigated under both On-Farm and Off-Farm sectors. Data envelopment analysis method is employed for the optimization of GHG emissions and energy flow. The highest and lowest energy consumption are related to tea and kiwifruit production, respectively. Results show that kiwifruit and eggplant have the highest scores in technical efficiency whilst tangerine and tea have the highest values in pure technical efficiency. The largest amount of energy is saved in kiwifruit orchards with 8316.29 MJ ha−1. Nitrogen fertilizer and diesel fuel have the topmost energy saving potential in most crops. Kiwifruit orchards have the highest potential for mitigation of GHG gas emissions (520.79 kg CO2 eq. ha−1). Results show that an appropriate usage of nitrogen fertilizer and replacement by organic fertilizer will mitigate GHG emissions as well as energy consumption. It can be concluded that GHG emissions can be mitigated by energy optimization in all the studied crops.

Optimal Energy Routing in Microgrids With IEC 61850 Based Energy Routers

The concept of energy Internet (EI) is very promising due to its ability to integrate distributed generators and storage devices. Energy routers (ERs) enable highly efficient, reliable, and flexible power system operation. To dynamically control and optimize energy flow in EI, an extensive communication is required. Considering the diversity of generators and ERs, it is crucial that such communication is achieved in a standardized and interoperable way. In this paper, an improved energy routing algorithm and its impact in EI operation are demonstrated. Furthermore, a full communication solution is developed for ERs and their interaction with grid operator. This paper proposes an International Electrotechnical Commission (IEC) 61850 communication-based power routing algorithm in microgrids. Optimal selection of source and routing path is achieved according to the proposed algorithm and communicated to the ERs via IEC 61850 based communication. The effectiveness of the proposed IEC 61850 communication-based routing algorithm is verified on network emulation platform comprising of IEC 61850 emulator tools and network simulator tool.

Energy Flow Optimization for Integrated Power–Gas Generation and Transmission Systems

This paper first presents a comprehensive model of the gas system with detailed formulations on pipelines, short pipes, resistors, valves, compressors, and compressor stations. Furthermore, an optimal energy flow model is proposed for integrated power-gas generation and transmission systems. Specifically, on the generation side, gas-fired units couple the two energy systems as the power generation and gas sink; on the transmission side, gas compressor stations link the two energy systems as the power demand and gas transportation. However, gas flow equations are nonlinear and gas flow directions also need to be optimized. Logical programming and tailored piecewise linearization techniques are performed, leading to a mixed-integer linear program (MILP). Only a logarithmic number of binary variables are introduced to represent the nonlinear quadratic function, and thus, the MILP model can be solved very efficiently. Numerical results on four power-gas test systems demonstrate the effectiveness of the proposed approach.

A new modeling and solution method for optimal energy flow in electricity‐gas integrated energy system

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity-gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity-gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20-node gas system combined with IEEE 30-bus test system.

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.

Probabilistic Optimal Energy Flow of District Multienergy Systems: An MPLP-Based Online Dictionary-Learning Approach

The integration of multienergy systems (MES) provides an important opportunity to increase the technical, economic, and environmental performance of the overall system compared with separately operated energy systems. The operation of district MES encounters numerous uncertainties such as fluctuations in energy prices as well as loads in different energy forms. In this paper, we formulate a probabilistic optimal energy flow (P-OEF) model of a district MES into a linear program (LP) problem based on the linear energy hub model. We propose an improved multiparametric LP (MPLP) based online dictionary-learning approach to efficiently calculate the P-OEF problem. We then derive a generalized critical region theory of the MPLP problem to jointly consider the change of parameters in terms of both the objective function and the constraints of P-OEF in MES. Finally, we explore the physical significance of the MPLP's critical region in the P-OEF problem. We perform a case study to verify the validity and effectiveness of the proposed approach compared to the traditional Monte Carlo approach for the P-OEF of the MES.

Whole-Process Pollution Control for Cost-Effective and Cleaner Chemical Production—A Case Study of the Tungsten Industry in China

In this research, a methodology named whole-process pollution control (WPPC) is demonstrated that improves the effectiveness of process optimization. This methodology considers waste/emission treatment as a step of the whole production process with respect to the minimization of cost and environmental impact for the whole process. The following procedures are introduced in a WPPC process optimization: ① a material and energy flow investigation and optimization based on a systematic understanding of the distribution and physiochemical properties of potential pollutants; ② a process optimization to increase the utilization efficiency of different elements and minimize pollutant emissions; and ③ an evaluation to reveal the effectiveness of the optimization strategies. The production of ammonium paratungstate was chosen for the case study. Two factors of the different optimization schemes—namely the cost-effectiveness factor and the environmental impact indicator—were evaluated and compared. This research demonstrates that by considering the nature of potential pollutants, technological innovations, economic viability, environmental impacts, and regulation requirements, WPPC can efficiently optimize a metal production process.

Industrial Symbiosis Dynamics, a Strategy to Accomplish Complex Analysis: The Dunkirk Case Study

Industrial symbiosis (IS) is presented as an inter-firm organizational strategy with the aim of social innovation that targets material and energy flow optimization but also structural sustainability. In this paper, we present geographical proximity as the theoretical framework used to analyse industrial symbiosis through a methodology based on System Dynamics and the underpinning use of Causal Loop Diagrams, aiming to identify the main drivers and hindrances that reinforce or regulate the industrial symbiosis’s sustainability. The understanding of industrial symbiosis is embedded in a theoretical framework that conceptualizes industry as a complex ecosystem in which proximity analysis and stakeholder theory are determinant, giving this methodology a comparative advantage over descriptive statistical forecasting, because it is able to integrate social causal rationality when forecasting attractiveness in a region or individual firm’s potential. A successful industrial symbiosis lasts only if it is able to address collective action problems. The stakeholders’ influence then becomes essential to the complex understanding of this institution, because by shaping individual behaviour in a social context, industrial symbiosis provides a degree of coordination and cooperation in order to overcome social dilemmas for actors who cannot achieve their own goals alone. The proposed narrative encourages us to draw up scenarios, integrating variables from different motivational value dimensions: efficiency, resilience, cooperation and proximity in the industrial symbiosis. We use the Dunkirk case study to explain the role of geographical systems analysis, identifying loops that reinforce or regulate the sustainability of industrial symbiosis and identifying three leverage points: “Training, workshop and education programs for managers and directors,” “Industrial symbiosis governance” and “Agreements in waste regulation conflicts.” The social dynamics aims for the consolidation of the network, through stakeholder interaction and explains the local success and failure of every industrial symbiosis through a system dynamics analysis.

Demand Response for Thermal Fairness in District Heating Networks

District heating (DH) networks are complex thermal grids wherein a centrally heated fluid is circulated through a network of pipes and heat exchangers to meet the heating needs of residential and commercial buildings. Several factors can lead to inefficient and unfair energy distribution among consumers in these networks. These include varying levels of building insulation, distance of individual buildings from the central energy source, and thermal losses in network pipes. Moreover, shortage of energy at the central energy source and extreme weather conditions can exacerbate these issues, leading to differing levels of thermal comfort and customer disgruntlement in the long run. In this paper, we propose and study a demand response scheme that attempts to ensure thermal fairness among energy consumers in modern thermal grids. We develop optimization formulations based on thermodynamic models of DH networks, which determine optimal energy flow for individual buildings in order to achieve thermal fairness across the network. Our numerical results using physics based models for DH networks show that it is indeed possible to achieve network level thermal fairness based objectives by controlling network parameters such as mass flow rates of water to the consumer premises and the supply water temperature.

Constructal enhancement of thermal transport in metal foam-PCM composite-assisted latent heat thermal energy storage system

The aim of this study is to augment thermal transport in latent heat thermal energy storage (LHTES) system by the optimum allocation of metal foam-phase change material (PCM) composite. This study emphasizes on the optimal volume and distribution of metal foam-PCM composite (MFPC) to enhance melting performance without delay in the total melting time. Therefore, a MFPC is designed according to constructal theory. The fundamental principle of the theory is to configure high thermal conductivity agents at optimal thermal energy flow path for effective heat exchange. A numerical code based on local thermal nonequilibrium approach equipped enthalpy porosity method is formulated, and evaluated. The results of the proposed configuration show that the provision of MFPC only at high local temperature gradient enhances the conductive transport with improvement in the overall thermal transport. It is derived that the elimination of metal foam volume at low temperature gradient incorporates the advantageous effect of natural convective transport, which is seen to be suppressed. Additionally, the proposed configuration may increase the volume of PCM, thus, the TES capacity. It also reduces the total weight and economy of energy storage system. The overall melting rate is improved by 11.11% in comparison with the LHTES with full volume of this high thermal conductivity agent.