Optimal Energy Flow Research Articles

Evaluating National Multi-energy System Based on General Modeling Method

This paper uses an integrated evaluation framework to evaluate the possible national multi-energy flow in China in a near future. The framework includes an integrated modeling for national multi-energy system in China. These key and bone national energy facilities are all modeled in a generalized network flow formulation. The modeling considers the regional energy system, inter-regional power and natural gas transmission system and the district supply heat model. Specific modeling method is abstracted as some general modeling rules to tackle the difficulty of modeling so many different energy facilities through focus on the input energy flow and output energy flow of them. In addition, it also contains a short-term or mid-term (1~3 years) operation evaluation model based on the least socio-economic cost or national energy resource adequacy. The model is proposed to find the potential of Chinese bone national energy system in an integrated multi-energy perspective and to evaluate the impact of a short-term or mid-term national energy policy in technical way considering the coupling of different energy sub-systems. Although it is difficult to verify the national energy model, the open and known energy data of China in 2016 is used to verify the model. Then this model is employed to evaluate the China multi-energy system in 2020 to give some suggestions on the optimization of energy flow at the national scale and supply heat transition.

ADMM-Based Multiperiod Optimal Energy Flow of A Regional Integrated Multi-Energy Microgrid

As the further development of regional integrated multi-energy microgrid, the management of microgrid operators is getting more difficult because of different interest parts and data privacy preservation. Thus, an ADMM-based multiperiod optimal energy flow (MOEF) model is presented in this paper, which is meaningful to research collaborative operation scheduling of multi-energy microgrid. Since the MOEF model considering constraints of multi-energy networks and energy conversion units are highly nonlinear and nonconvex, a second-order cone relaxation and incremental linearization method are adopted to mathematically transform it into a solvable mixed integer second-order cone programming (MI-SOCP). A modified regional integrated multi-energy microgrid case is evaluated to demonstrate the effectiveness of the proposed model and method.

Distributed optimization of energy flows in microgrids based on dual decomposition

Abstract In this paper, a transactive control technique, dual decomposition (DD), combined with a reinforcement learning algorithm, fitted Q-iteration (FQI), is proposed to control the power injected into the main utility grid by a microgrid. To maintain a stable grid connection and provide flexibility services to the distribution system operator, the microgrid manager can control the power injected to the grid by microgrid users with a price signal computed using DD. This work focuses on how batteries located at the homes of microgrid users can be used to minimize the amount of power injected to the grid. The operation of the battery is controlled using FQI. The effect of battery sizes and charging rates on the amount of flexibility available is also analysed. Our approach is evaluated by considering a microgrid with 7 residential users each containing a battery, a photovoltaic installation and a non-controllable base load. Data used for the simulations is obtained from Belgian residential users. Simulation results show that with DD, a 10% increase in power injected to the grid is obtained compared to a theoretical optimal benchmark. These results show the performance of the proposed method while preserving user privacy: inaccessible user preferences/devices. Also, the simulation results show that batteries can be used as flexibility providers to control the amount of power injected to the grid despite their operational constraints. Moreover, these results provide an insight for considering DD combined with FQI in scenarios where several community microgrids interact with each other and the main utility grid.

Complementary Configuration and Optimal Energy Flow of CCHP‐ORC Systems Using a Matrix Modeling Approach

A mass of thermal energy from the combined cooling, heating, and power (CCHP) system will be wasted by the buildings with low ratio of heat to power demand. To further improve the using efficiency of the excess thermal energy, an active method, which adds the organic Rankine cycle (ORC) equipment in the CCHP system, is configured. A complementary configuration of this CCHP‐ORC is also investigated. This paper presents a matrix modeling approach to establish a mathematical model of the CCHP‐ORC system. The CCHP‐ORC system can be viewed as a multiple input and multiple output (MIMO) model. Energy conversion of the system components is described as the efficiency matrices. Energy flow between the system components is described as dispatch matrices. Energy conversion and flow from the system input to output are modeled by a conversion matrix. The objective function and the constraints of the system are determined. The optimal operation strategies are obtained by solving the optimization problem of minimizing the evaluation criteria function. Comprehensive case studies are conducted based on a hypothetical CCHP‐ORC system. The study results reveal that the effectiveness and economic efficiency of the proposed approaches outperform those obtained from conventional CCHP systems.

Optimal performance of CCHP based microgrid considering environmental issue in the presence of real time demand response

In this article, an improved system in micro energy grid is investigated composing of energy hub system as combined cooling, heating and power (CCHP) based microgrid integrated with renewable energies like photovoltaic (PV) and wind turbine (WT). The main goal is to solve a multi-objective model that includes reducing carbon emission and operation cost in the presence of real time demand response program (DRP). The flexibility of suggested energy hub system is the result of utilizing three hubs, storage devices and optimal energy flow. The multi-objective model has been solved using weighted sum approach and max-min fuzzy to make the optimal Pareto solutions and select the trade-off solution, respectively. This paper implemented the priced based real time DRP to flatten the load curve by shifting some amounts of loads from peak periods to off-peak periods. Two cases studied in the article that indicates the effects of real time DRP on optimization of energy hub systems. Finally, the energy hub system model is formulated and solved using CPLEX solver in GAMS optimization software. The results show that the operation cost and carbon emission are reduced 3.97% and 2.26%, respectively due to implementation of real time DRP.

Understanding the Evolution of Industrial Symbiosis with a System Dynamics Model: A Case Study of Hai Hua Industrial Symbiosis, China

Industrial symbiosis (IS) is a key sub-field in industrial ecology. The field itself assists in developing strategies that support local and regional sustainable development. The evolution of IS is an important topic to be explored. In this paper, we use the system dynamics (SD) method to establish a model of the evolution of industrial symbiosis in the case of Hai Hua Industrial Symbiosis (HHIS). In this model, different scenarios for energy consumption, solid waste utilization, and wastewater utilization were set by changing the parameter values of certain input variables to analyze the evolution of HHIS. In addition, the drivers for IS evolution were investigated qualitatively in this study. The SD model was helpful for visualizing the effects and benefits of reducing the energy consumption, solid waste stock, and wastewater stock that were achieved by establishing symbiotic relationships during the evolution of IS. The results show that the optimization of energy and material flows and other symbiotic benefits can be achieved in HHIS. There are still some challenges that restrict the further evolution of HHIS. Some suggestions are proposed to promote its further evolution.

Homogenized adjacent points method: A novel Pareto optimizer for linearized multi-objective optimal energy flow of integrated electricity and gas system

This paper constructs a novel multi-objective optimal energy flow of an integrated electricity and gas system to fully exploit complementary benefits of the system. To ensure a reliable convergence, an incremental piecewise linearization is employed to linearize the original nonlinear problem into a mixed integer linear programming. Actually, the proposed issue represents a highly constrained optimization with numerous variables and multiple conflicting objectives. To effectively solve the problem, a novel analytical Pareto optimizer called homogenized adjacent points method (HAPM) is first proposed, which aims to obtain a Pareto solution set with three basic strategies, including axes homogenization, adjacent points calculation and adjacent points filtration. Compared to the existing methods, the major superiority of HAPM is that the algorithm can obtain a full distribution of Pareto solution set with the boundary of Pareto front and non-dominated quality of the solutions. Finally, a best compromise solution is identified from the Pareto solution set using an entropy weight based ideal point method. Simulation results indicate an environmentally friendly and reliable integrated electricity and gas system is realized with a slight cost sacrifice, and validate the robustness of HAPM to obtain a non-dominated, uniform and widespread Pareto solution set compared with existing methods.

Determining energy storage amount for development of novel microgrid energy flow optimization system with photovoltaic energy generation

A new type of inexpensive and easily deployable microgrid management system is being developed for optimization of grid transmission for outlying small energy consumers who occasionally need high power levels for short time periods. Initial research and preliminary analysis of a pilot project was carried out to determine the parameters of the system. The available historical data for PV production and energy consumption were collected and analyzed and energy flow profiles were developed. The algorithm of energy flow priorities was created for the energy shortage coverage from combined use of PV and battery storage system. The worst-case scenario was analyzed to prevent critical system failures. The simulations determined that the pilot site grid connection power level can be safely halved by installing 36 kW PV power combined with 24 kWh EV battery pack for energy storage.

Chance-Constrained Optimization for Multienergy Hub Systems in a Smart City

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish–Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.

Independence without control: Autarky outperforms autonomy benefits in the adoption of private energy storage systems

Decentralized energy storage systems (ESS) are a promising means to more effectively match the supply and demand of fluctuating renewable energies. In most countries, however, ESS market share is small and whether or not the technology will attain a critical market share is subject to homeowners' investment decisions. For policy and industry alike, it is of particular interest to identify factors that drive ESS adoption. Empirically addressing this question, we hypothesized that the factors autarky and autonomy aspirations crucially determine ESS adoption decisions. In two studies (Ntotal = 489), sketching future decentralized energy scenarios, we found evidence for the importance of both factors for homeowners' evaluations of the technology. However, only autarky significantly affected homeowners' willingness to pay extra for ESS, in that homeowners invested more in the technology when autarky was higher (Study 1) or autarky benefits were emphasized (Study 2). In accordance with concepts aspiring to optimize energy flow on the low-voltage grid level (e.g. Smart Neighborhoods), we additionally examined the influence of autarky and autonomy aspirations on homeowners' willingness to exchange self-generated energy within a local energy network. Results showed that emphasis on autarky increased the subjective value of self-generated energy, decreasing the likelihood of peer-to-peer energy trading.

Decentralized Optimization of Multi-Area Electricity-Natural Gas Flows Based on Cone Reformulation

A large-scale integrated energy system can represent several subsystems representing areas that are tied by electricity and natural gas networks. Accordingly, we propose a decentralized optimal energy flow (DOEF) calculation as compared with a centralized solution method. The proposed approach demonstrates the merits of the decentralized operation and control of a multi-area integrated electricity-natural gas system (IEGS), in terms of large-scale modeling requirements, faster computations, and data management for local sensitivity analyses. Using the proposed decentralized structure, the communication burden is relatively light as individual area operators in a multi-area IEGS will make optimal dispatch decisions independently and the corresponding information is shared with adjacent subsystems. The reformulation of the second-order cone (SOC) is proposed using advanced sequential cone programming (SCP) to handle the nonlinear steady-state natural gas flow, which provides a feasible solution with a high degree of computational efficiency. Furthermore, an iterative alternating direction method of multipliers (I-ADMM) is adopted to manage the nonconvexity of integer variables, which guarantees a satisfactory convergence performance. Case studies on three multi-area IEGS validate the effectiveness of the proposed model in a multi-area IEGS.

A Battery Management System for Efficient Adherence to Energy Exchange Commands Under Longevity Constraints

This paper is focused on the problem of controlling the energy storage such that the energy exchange between the storage and the remaining system is performed in the required amount with maximum efficiency, and that all the constraints for the storage system longevity are adhered to. There is a huge need for such storage management in energy flow optimization of microgrids where energy flows from/to storages are planned in order to achieve maximum gain in microgrid operation. The case of a valve-regulated lead-acid battery is elaborated. The underlying computation tractability and the performance of such a battery management system is demonstrated both in simulation and experimentally.

Optimal Energy Flow Calculation of Combined Natural Gas, Cooling, Heating and Power Joint Supply Park Microgrid

The combined natural gas, cooling, heating and power joint supply (CNCHP) microgrid of an industrial park has the advantages of cascaded use of energy, which can effectively improve energy efficiency and reduce pollutant emissions. In this paper, the optimal energy flow calculation model of CNCHP microgrid is established. Minimizing the total running cost of the microgrid is as the objective function, and the network operation characteristics of the cooling network, heating network, power supply network and gas supply network is considered. The model includes the loss characteristics and security operation constraints of various types of energy networks, and the obtained optimal operation schedule can meet the actual operational requirements of the microgrid. Taking a CNCHP microgrid of an industrial park as an example, the correctness and validness of the proposed optimal calculation model are verified. The results show that the optimal operation scheme of the CNCHP microgrid can effectively reduce the operating cost and improve the energy efficiency.

Optimal energy flow and nodal energy pricing in carbon emission-embedded integrated energy systems

Green energy is driving the evolutions of energy industry and carbon emission is becoming an important concern. Considering the increasing couplings among various energy sectors, this paper investigates multi-period optimal energy flow and energy pricing in carbon-emission embedded integrated energy systems, including electricity, natural gas, and district heating networks. Firstly, an optimal scheduling model of integrated energy systems was proposed in this paper. The models of DC power flow, natural gas pipeline flow and heating network energy flow are presented and linearized for the optimization problem. Natural gas-fired generators and combined heat and power (CHP) units are modeled as coupling components of electricity-gas and electricity-heating networks. Then, based on the optimal scheduling model, the locational marginal prices (LMP) for electricity, natural gas and heating network are determined. Moreover, the carbon emission caused by energy production has been taken into account in the optimal scheduling and energy pricing process. Finally, case studies on a combined network consisting of IEEE 39-bus system, Belgium 20-node natural gas system and 6-node heating system demonstrate the effectiveness of the proposed model and the impacts of carbon emission on system scheduling and LMP.

Energy transfer and correlations in cavity-embedded donor-acceptor configurations

The rate of energy transfer in donor-acceptor systems can be manipulated via the common interaction with the confined electromagnetic modes of a micro-cavity. We analyze the competition between the near-field short range dipole-dipole energy exchange processes and the cavity mediated long-range interactions in a simplified model consisting of effective two-level quantum emitters that could be relevant for molecules in experiments under cryogenic conditions. We find that free-space collective incoherent interactions, typically associated with sub- and superradiance, can modify the traditional resonant energy transfer scaling with distance. The same holds true for cavity-mediated collective incoherent interactions in a weak-coupling but strong-cooperativity regime. In the strong coupling regime, we elucidate the effect of pumping into cavity polaritons and analytically identify an optimal energy flow regime characterized by equal donor/acceptor Hopfield coefficients in the middle polariton. Finally we quantify the build-up of quantum correlations in the donor-acceptor system via the two-qubit concurrence as a measure of entanglement.

Fuel and Energy System Control at Large-Scale Damages. II. Optimization Problems

In continuation of [1], the capabilities of the network flow modeling of resource supply processes are investigated in the case of a deterioration in their infrastructural properties. Within the multivariable model [1], energy supply options in spatially distributed systems after destructive impacts are analyzed. Mathematical formulations of the problems of optimization of energy flows in a damaged network are proposed. The strategies for controlling the flows based on a posteriori information on the change in the capacity of the arcs of the model network are determined. The control objective is the providing the users requirements as much as possible taking into account the regulatory constraints on the feasible levels of the accident/load on the network subsystems. This problem is considered as a multicriterion (MO) one. Different methods for the convolution of the criteria are analyzed depending on the emphasis attached to the content of the formulation.

Robust optimization for improving resilience of integrated energy systems with electricity and natural gas infrastructures

The integration of natural gas in electricity network requires a more reliable operating plan for increasing uncertainties in the whole system. In this paper, a three-stage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks. Non-convex constrains are linearized in order to formulate the dual problem of optimal energy flow. Then, the proposed three-stage problem can be reformulated into a two-stage mixed integer linear program (MILP) and solved by Benders decomposition algorithm. Numerical studies on IEEE 30-bus power system with 7-node natural gas network and IEEE 118-bus power system with 14-node natural gas network validate the feasibility of the proposed model for improving resilience of integrated energy system. Energy storage facilities are also considered for the resiliency analysis.

Dynamic Optimal Energy Flow in the Integrated Natural Gas and Electrical Power Systems

This paper focuses on the optimal operation of the integrated gas and electrical power system with bidirectional energy conversion. Considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated gas and power systems. With proper assumptions and simplifications, the problem is transformed into a single-stage linear programming to obtain the optimal operation strategy for both gas and power systems. Simulation on the test case illustrates the success of the modeling and the beneficial roles of the power-to-gas are analyzed. The proposed model can be used in the decision support for both planning and operation of the coordinated natural gas and electrical power systems.

Decoupled Optimization of Integrated Energy System Considering CHP Plant Based on Energy Hub Model

By optimally interacting electricity, natural gas, heat, cooling and fuels systems, the integrated energy system (IES) represents a significant opportunity to increase technical, economic and environmental performance compared with classical energy systems. These performance improvements can take place at both the operational and the planning stages. The utilizations of combined heat and power (CHP) units, heat pumps and electric boilers create linkages between different energy supply systems. However, these independent sub-systems are in the charge of different departments need to be concerned, which complicates the optimal dispatch of IES. Regarding this issue, a hierarchical decoupling algorithm is developed to calculate the optimal energy flow of IES. The proposed algorithm requires less iteration because the optimal procedure in sub-systems are operated hierarchically and separately. Case studies are performed based on an IES test system which combines the IEEE 33 bus test system and the Barry Island district heating network. Results testifies the performance of the proposed approach and demonstrates that both electrical and heat network can be affected by the CHP penetration.

A goal programming based model system for community energy plan

Community energy system optimization model has great contribution to formulate community energy planning indexes. But an inappropriate response of uncertainty always makes such “optimal plan” work ended in nothing. It is still a herculean task to solve a hybrid programming model which contains stochastic and fuzzy parameters. In order to acquire more flexible and reliable energy planning indicators in a convenient way, a goal programming based model system (GPMS) is proposed to conduct dynamic variation analysis of community energy flow. GPMS contains general linear programming model, goal programming model and grey relational degree model for results analysis. General linear programming model is used to calculate optimal community energy flow on baseline situation. Deviational variables associated with each independent parameter and total fossil energy consumption (TFEC) are introduced in goal programming model. Many kinds of optimum community secondary energy flow maps can be acquired by adjusting the weight which has been given to TFEC’s deviation variables. The grey correlation degree, a measure of relevancy between two data series, is used to evaluate these optimum community energy flow results. At last, this GPMS for community energy plan is introduced, as well as a case study in Tianjin.