Economic Optimization Research Articles

Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid

This article describes the development of an optimal and predictive energy management system (EMS) for a microgrid with a high photovoltaic (PV) power contribution. The EMS utilizes a predictive long-short-term memory (LSTM) neural network trained on real PV power and consumption data. Optimal EMS decisions focus on managing the state of charge (SoC) of the battery energy storage system (BESS) within defined limits and determining the optimal power contributions from the microgrid components. The simulation utilizes MATLAB R2023a to solve a mixed-integer optimization problem and HOMER Pro 3.14 to simulate the microgrid. The EMS solves this optimization problem for the current sampling time (t) and the immediate sampling time (t+1), which implies a prediction of one hour in advance. An upper-layer decision algorithm determines the operating state of the BESS, that is, to charge or discharge the batteries. An economic and technical impact analysis of our approach compared to two EMSs based on a pure economic optimization approach and a peak-shaving algorithm reveals superior BESS integration, achieving 59% in demand satisfaction without compromising the life of the equipment, avoiding inexpedient power delivery, and preventing significant increases in operating costs.

The techno-economics of transmitting heat at high temperatures in insulated pipes over large distances

This study reports a systematic techno-economic assessment of the cost-optimal transmission for air as the heat transfer fluid at temperatures of up to 1200 °C, at scales of 1 to 1000 MW and at distances of up to 10 km. It employs a steady state heat transfer analysis, with energy balances, to assess the effect of scale, temperature and insulation on the heat losses and efficiency of the thermal transmission system, following by a techno-economic assessment. The sensitivity of the Levelised Cost of Heat, LCOHtr, to variations in thermal scale, operating temperature, distance, refractory and insulation thickness, ratio of the thickness of refractory and insulation, cost of any supplementary heat and lifetime is reported. The results show that LCOHtr decreases with an increase in thermal scale, as expected. The role of insulation is much more complex, since increasing the thickness of thermal barrier increases both cost and efficiency of the transmission, requiring an economic optimum to be determined for each of the various conditions assessed. Parameters are also coupled because a higher cost of supplied energy/ heat justifies the use of more insulation material. For a large GW scale thermal system, we estimate that it is possible to achieve a minimum overall LCOHtr,min of 0.16–0.36 USD/GJ/km of the length of the thermal transmission system, excluding additional location-specific costs such as land-access and local construction costs. These estimated costs are sufficiently attractive to justify ongoing development of systems to transport renewable heat to industry from sources such as concentrated solar thermal energy.

An optimal siting and economically optimal connectivity strategy for urban green 5G BS based on distributed photovoltaic energy supply

The emergence of ultra-dense 5G networks and a large number of connected devices will bring with them significant increases in energy consumption, operating costs, and CO2 emissions. At the same time, the deployment of distributed photovoltaic (DPV) in megacities plays an important role in promoting the integration of “building-photovoltaic”, adjusting the city's clean energy supply system, and building a green, and low-carbon smart city. The development of a new “DPV-5G Base Station-Energy Storage (DPV-5G BS-ES)” coupled DC microgrid system and its pre-deployment investment costs are fundamental factors to be considered when the problem of large-scale DPV and BS deployment in cities has to be addressed. There are almost no published papers on this issue in the literature, which only consider the problem of maximizing the deployment of 5G BS service coverage or only perform optimization analyses for the performance of microgrid systems without considering the economic optimality and low-carbon green issues in the construction of urban utilities. In order to solve the problem of 5G BS deployment and economic optimization of topology in the “DPV-5G BS-ES” coupled DC microgrid system, a novel deployment strategy is proposed in this paper. The strategy is divided into: (a) The Geographic Information System (GIS) for extracting and analyzing 3D data from all buildings in the area. (b) A GIS-based formulation of a mixed integer quadratic constrained planning model (BSSCP) under discrete fixed radius coverage constraints is used to delineate building clusters within the area and determine the optimal deployment location of 5G BS under each sub-cluster. (c) An Improved Self-Organizing Mapping (ISOM) model combines GIS and microgrid performance parameters to address the economically optimal connection of grid lines for DPV in 3D space. (d) Simulation experiments were conducted for the power output and profitability of eight different PV panels and tracking systems to calculate the economically optimal type of DPV generation hardware. The experimental results show that the BSSCP model proposed in this study reduced the 5G BS deployment cost input by up to 35.8974 % compared to that of the control experimental model. Meanwhile, the ISOM model obtained an average reduction of 52.3741 % in the cost of connecting cables for the optimized 25 DPV planning and DC microgrid topology.

Optimal scheduling of microgrids considering real power losses of grid-connected microgrid systems

Energy conservation, emission reduction and vigorous development of new energy are inevitable trends in the development of the power industry, but factors such as energy storage loss, solar energy loss and line loss in real power situations have led the problem to a complex direction. To address these intricacies, we use a more precise modeling approach of power loss and propose a collaborative optimization method integrating the Deep-Q-Network (DQN) algorithm with the multi-head attention mechanism. This algorithm calculates weighted features of the system’s states to compute the Q-values and priorities for determining the next operational directives of the energy system. Through extensive simulations that replicate real world microgrid (MG) scenarios, our investigation substantiates that the optimization methodology presented here effectively governs the distribution of energy resources. It accomplishes this while accommodating uncertainty-induced losses, ultimately achieving the economic optimization of MG. This research provides a new approach to deal with problems such as energy loss, which is expected to improve economic efficiency and sustainability in areas such as microgrids.

Under the Background of Economic Structure Optimization, Environmental Governance Promotes Macroeconomic Structural Adjustment Measures

Along with the implementation of reform and opening up in the 1970s, China’s economy entered a period of rapid development. During this period, the economic structure of our country has gradually changed from the primary industry to the secondary industry. Even today, the industrial industry still plays a very important role in the economic structure of our country. The industrial chain such as the real estate industry, chemical industry, and manufacturing industry is still the main industrial structure that promotes our economic growth for a long time. However, as the energy of industrial development is obtained by massive consumption of fossil fuels, the economic development brought by industrial development is behind the serious environmental pollution problem. We gradually realize that economic development cannot be based on environmental damage, but although our country has been committed to reversing the situation of environmental sacrifice for economic development at the beginning of the discovery of problems, the effect is very little. Therefore, how to maximize environmental protection under the current economic structure is the issue to be discussed in this paper. This paper mainly summarizes relevant experience through domestic and foreign literature, and clarifies the macro relationship between environmental governance and economic structure, and then discusses it from three dimensions: technological innovation, consumer demand, and international trade. The final results show that different types of environmental regulations have different effects on industrial structure, and the effectiveness of policy incentive regulation is about 23% higher than that of command control regulation. At the same time, according to the analysis results of economic structure upgrading in the Pearl River Delta region, the effect of environmental regulation on the regional industrial structure is also different, and the average effect can improve the overall industrial structure optimization rate by about 36%.

An analytical target cascading method-based two-step distributed optimization strategy for energy sharing in a virtual power plant

Large-scale distributed renewable energy sources as well as emerging controllable loads such as electric vehicles connected to the distribution grids have posed challenges to the efficient management of these distributed energy resources. Promoting energy-sharing of distributed energy resources through economic incentives is a cost-effective and equitable solution to the challenge. This paper proposes a two-stage transactive energy control mechanism by the virtual power plant (VPP) for energy-sharing of multiple prosumers. Firstly, a VPP internal price mechanism based on the supply-demand ratio of prosumers in the energy-sharing alliance is proposed. Then, a two-stage economic optimization model is developed. Trading prices between VPP and prosumers are established in the first stage, and prosumers make operating plans based on those prices in the second stage. Then, considering that prosumers and VPP are independent stakeholders, a decentralized optimization algorithm based on the analytical target cascading method is developed. The upper-level VPP coordinates the energy-sharing alliance and sets the transaction price following the overall supply and demand ratio. Lower-level prosumers independently make decisions and feedback on the trading power of the VPP. Then, iterative calculations provide a distributed and independent solution. Finally, the case study verifies that the proposed energy-sharing model can reduce the operating cost of prosumers by 14.29 %. Moreover, prosumer privacy protection is achieved by the analytical target cascading-based distributed model at a cost of less than 1.5 % accuracy.

Research on the calculation method of the reasonable utilization rate of renewable energy considering generation–grid–load–storage coordinated planning

The continuous advancement of the dual carbon goal will lead to fundamental changes in China’s energy structure, with renewable energy emerging as the dominant force in the power supply system. As the proportion of installed capacity for renewable energy continues to increase, the absorption capacity and reasonable utilization rate of renewable energy will become a concern for all sectors of society. At present, the degree of utilization of renewable energy has emerged as a significant factor limiting the development of renewable energy sources. Due to variations in the power structure, load characteristics, and flexible adjustment resources across different regions, blindly pursuing full absorption or higher utilization of renewable energy as a goal can result in increased reserve costs for the system and limited economic benefits, and hinder the development of renewable energy. Therefore, the present study develops a generation–grid–load–storage collaborative planning model aimed at achieving economic optimization by setting different renewable energy utilization rates and obtains the installed capacity of renewable energy and storage under different conditions in the planned year of Gansu Province. The economic analysis indicates that the optimal utilization rate of renewable energy in Gansu Province is projected to decrease from 100% during the period of 2024–2028 to approximately 90% from 2040 to 2060. It serves as a valuable tool for informing the planning and economic development of renewable energy and storage initiatives in Gansu Province, facilitating the achievement of its dual-carbon objectives.

Integrated energy system for low-carbon economic operation optimization: Pareto compromise programming and master-slave game

An integrated energy system (IES) can effectively solve the energy crisis, realize multi-energy complementarity, and promote fine-grained energy development. Aiming at the low-carbon economy problem of IESs with combined heat and power (CHP), carbon capture systems (CCSs) and power-to-gas (P2G), the joint operation mode of CHP-CCS-P2G is proposed, and the output characteristics and carbon emissions of CHP units under this mode are analysed. On the supply side, a multi-objective optimization method based on compromise programming is proposed to solve the contradiction between economic and environmental objectives in the IES, and the energy operator balances economic and environmental performance to obtain the optimal configuration scheme and operation strategy. On the load side, a master-slave game energy trading strategy considering integrative demand response (IDR) is adopted to balance the interests of the energy operator and users centrally managed by the load aggregator to ensure the fairness of transactions between the two parties. The results show that the overall revenue of the energy operator decreases by 7.99 % after considering IDR. However, the carbon emissions decreased by 933.82 kg, the carbon trading revenue increased by 45.18 %, and the energy purchase cost of users decreased by 9.34 %.

Technical and economical design and optimization of a solar-biodiesel collector system to supply electricity and drink water in AbuMusa Island

A hybrid renewable energy system including microalgae cultivation ponds to produce biodiesel. The necessary heat for the biodiesel production system is provided by microalgae through a solar parabolic linear collector, and the water temperature of the ponds, which is supplied from the sea, is provided by a geothermal system. Additionally, the linear collector is utilized to supply the necessary heat for the solar water desalination system. All the simulations of the mentioned renewable systems, except for the solar collector, were carried out separately in ASPEN HYSYS software, and the solar collector was simulated in Fortran software. The results were utilized in the ASPEN HYSYS. The simulation results show that to supply 225 kW of power through a diesel generator, 492 microalgae cultivation ponds are needed and the share of biofuel in the power supply is 5 %. The biodiesel reactor produces fuel at a cost of 1.45 $/L and has an efficiency of 79.8 %. Also, by simulating the solar collector in the Fortran and using its results in solar water desalination, the minimum and maximum produced drinking water are 358 l/h in August and 609 l/h in October, respectively. The price of water produced is 3.45 $/m3.

Research on Regional Energy System Planning Considering Flexible Access of Electric Vehicles

The high proportion of renewable energy sources and intermittent loads pose challenges to the flexible operation of regional energy systems. To improve the flexible operation level of regional energy system, this paper considers the uncertainty of energy storage charging and discharging, demand-side management, and V2G of electric vehicles, and establishes a flexible resource supply capacity and cost model. With the goal of minimum flexibility demand and economic optimization, the implementation of flexible supply and demand balance constraints is added. The advantages and disadvantages of the traditional resource scheduling scheme and the unified flexible resource scheduling method are compared from the net load volatility, the maximum operating volatility and the whole time scale flexible supply and demand balance index. The algorithm analysis proves the advantages of the proposed scheduling scheme in improving the flexibility of the regional energy system, which can describe the flexibility of the regional energy system well and can be quantitatively characterized.

Enhanced Multiobjective Optimization Algorithm for Intelligent Grid Management of Renewable Energy Sources

Optimal scheduling of microgrids (MGs) is a crucial component of smart grid optimization, playing a vital role in minimizing energy consumption and environmental degradation. However, existing methods tend to consider only a single optimization and do not consider the multiobjective optimization problem of MGs in a comprehensive and integrated way. This study proposes a comprehensive multiobjective optimal scheduling methodology for renewable energy MGs, incorporating demand-side management (DSM) considerations. Initially, a DSM multiobjective optimization model is formulated, focusing on the load shifting of controllable devices within the MG to refine the electricity consumption structure. This model contemplates the renewable energy consumption of the MG, customer electricity purchase costs, and load smoothness. Subsequently, a multiobjective optimization model for grid-connected MGs, encompassing wind and photovoltaic power generation, is constructed with the dual objectives of economic and environmental optimization for the MG. Ultimately, a multimodal multiobjective optimization algorithm, amalgamating a local convergence index and an environment selection strategy, is proposed to solve the model. The experimental results show that compared with other methods, the proposed method in this paper can reduce the integrated cost by 32.6% and 38.9% in summer and 19.4% and 40.2% in winter. This stands out as a unique contribution in the field of MG optimization, as it integrates DSM considerations into a multiobjective optimization model. This methodology achieves a balance between minimizing energy consumption and environmental degradation while also enhancing economic efficiency.

SustainableTech – A Holistic Framework for Integrated Techno-Economic and Sustainability Assessments

A methodology is proposed to perform integrated techno-economic and life-cycle assessments of new technologies and processes. The methodology consists of a framework for decision-support considering both economic and environmental aspects in a streamlined way. This can support decision-makers to identify optimal value chains for products and activities with the aim of yielding improved economic margins and good environmental performance. Further, because of its holistic and systematic approach, the framework may provide customers with an increased awareness of trade-offs between environmental indicators and economic earnings along the value chains for different market scenarios. The developed methodology is applied to a case study involving the optimal planning for sustainable energy generation in buildings. The research expanded the original case study by incorporating additional 16 life-cycle assessment midpoint impact categories, using an updated background database, and improved techno-economic assumptions. Through this expanded analysis, alternative solutions were identified, Specifically targeting improvements in the climate change impact by exploring different permissible variances from the economic optimum.

Application of Degradation and Optimization Models for digitization of Maintenance Management in Railway Infrastructures

This study presents an innovative methodology to improve the maintenance of railway infrastructures, integrating degradation models and economic optimization. The combination of a degradation model and a semi-Markovian model supported by the Weibull function, calculates the economically optimal degradation, to release the preventive intervention. The development of digital twins of assets and infrastructures makes possible the integration of online data and the incorporation of models that help the maintenance manager in decision-making. Organizations must determine the information to be collected from the assets. Transform the information into inputs for the models and, after their application, would allow them to identify the most economically favorable global maintenance scenarios. This work evolves the decision support on preventive maintenance implementation in the railway sector. From a decision table, used by railway managers, with three levels of risk established subjectively on an experimental basis, it evolves to the objective methodology, designed to determine the optimal degradation. The methodology can be applied to assets by maintenance managers but can also be incorporated into the digital twins. The case study illustrates the practical application of this methodology to the degradation of railway track sections. The research emphasizes the importance of data-driven decision-making for economic maintenance management. However, other priorities such as transportation safety and efficiency could complement the economic one.

Design optimization method of heating and cooling full cycle storage air conditioning in integrated energy system

Heating and cooling storage air conditioning is an important part of the integrated energy system, which can play an important role in the flexible conversion of thermoelectric coupling systems. Because storage air conditioning involves the dynamic characteristics of power supply on the source side and cold and heat demand on the load side, system capacity allocation is a problem of technical and economic optimization. This paper breaks through the traditional design method based on the hourly load distribution law of typical design days and proposes a performance-oriented design method for the bidirectional full-cycle dimension storage air conditioning system based on the system’s annual operation performance, which can take into account the hourly dynamic load distribution law of every day in the full heating season and the full cooling season coupled with the bidirectional demand of cold and heat loads. A more scientific and reasonable capacity configuration scheme for the storage air conditioning system is obtained. The design is presented with a concrete case.

Boosting of Dissipated Renewable Energy Systems Towards Sustainability in Kazakhstan

Abstract The study aims to develop a methodology for selecting and justifying a multi-energy hub based on renewable energy sources for agricultural complex. The methodology has an international dimension and was tested for the pilot case of the study in an agricultural site in Kazakhstan. The methodology consists of two parts. With the help of the EnergyPro software package, simulation of technical and economic analysis and optimization of energy hub operation for several different energy generation units was carried out. During the simulation, four different scenarios of an energy hub based on solar and wind energy, biomass and heat pump as well as coal-based fossil energy sources were considered. The second part of the methodology was based on the economic justification of climate-neutral technological solutions using multi-energy hubs in the agriculture sector. Climate neutrality diagram was created by use of GHG emission trading experience for a detailed technical and economic analysis and selection of the best renewable sources from various installation sites. Results show that the most promising and cost-effective option is the scenario with an integrated wind park, heat pump and biomass boiler.

Economic Analysis and Optimization of Energy Storage Configuration for Park Power Systems Based on Random Forest and Genetic Algorithm

Economic Analysis and Optimization of Energy Storage Configuration for Park Power Systems Based on Random Forest and Genetic Algorithm

Economical quasi-Newton unitary optimization of electronic orbitals.

We present an efficient quasi-Newton orbital solver optimized to reduce the number of gradient evaluations and other computational steps of comparable cost. The solver optimizes orthogonal orbitals by sequences of unitary rotations generated by the (preconditioned) limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm equipped with trust-region step restriction. The low-rank structure of the L-BFGS inverse Hessian is exploited when solving the trust-region problem. The efficiency of the proposed "Quasi-Newton Unitary Optimization with Trust-Region" (QUOTR) solver is compared to that of the standard Roothaan-Hall approach accelerated by the Direct Inversion of Iterative Subspace (DIIS), and other exact and approximate Newton solvers for mean-field (Hartree-Fock and Kohn-Sham) problems.

Change in the pattern of urban-rural economic circulation and optimization in the Jianghan Plain region

Change in the pattern of urban-rural economic circulation and optimization in the Jianghan Plain region

Research on dynamic spillover effect of stock market volatility in Beijing-Tianjin-Hebei region

To enhance economic cooperation in the Bohai Sea and Beijing-Tianjin-Hebei region, China introduced the Beijing-Tianjin-Hebei integration plan in 2014. Over the past decade, coordinated development has established a solid foundation, emphasizing economic and spatial structure optimization. The region’s development has now achieved comprehensive, qualitative, and profound progress. Recognizing economic integration as crucial, this study analyzes the 2020-2023 stock data of listed firms in the region, utilizing the DY spillover index to examine sectoral volatility spillover effects, both statically and dynamically. Results reveal bidirectional, asymmetric spillovers, with heterogeneous spillover levels. Industry, raw materials, and information technology are key spillover sectors, significantly influencing others and inducing synchronization. Property and finance sectors are primarily recipients, influenced by others. Energy and communication services exhibit strong bidirectional spillovers. Volatility spillover strength is influenced by factors such as inter-sector correlation, market risk, macroeconomic changes. Studying these factors helps grasp volatility spillover patterns and understand sectoral volatility-factor relationships.

Economic Nonlinear Model Predictive Control of Continuous Viral Bioreactors

Viral particle systems are integral parts of modern biotechnology, finding use in vaccines, drug delivery platforms, and recombinant protein production. Continuous manufacturing of these systems can offer improved manufacturability and quality control. However, viral systems often have complex kinetics which can introduce undesirable process dynamics and lower product titers in continuous operation. This article explores the use of economic nonlinear dynamic optimization and model predictive control to achieve multiple process objectives such as maximizing productivity and/or purity. Economic nonlinear model predictive control is also demonstrated to robustly control the bioreactor under plant-model mismatch in different scenarios.