Optimal System Operation Research Articles

Heating performance of PCM radiant floor coupled with horizontal ground source heat pump for single-family house in cold zones

As the energy demand for space heating increases, the necessity for innovative and energy-efficient heating technologies has become more urgent. This study proposes and evaluates a heating system that integrates a phase change material radiant floor with a horizontal ground source heat pump (PCM floor-HGSHP, Case 1), intending to maintain indoor comfort temperatures and enhance energy efficiency through the utilization of renewable energy sources. Despite the considerable potential of PCM floors, their combination with HGSHP requires further research to fully unlock their benefits. The study involved simulating the PCM floor using a validated TRNSYS component and developing an advanced rule-based control strategy with time-of-use tariffs as a key input to optimize system operation. To elucidate and quantify the advantages of the PCM floor-HGSHP system and verify its applicability in cold regions of China, a single-family house was used as a case study building, four representative cities were selected, and two comparative simulation cases were defined, along with three key performance indicators. The results indicate that, compared with the radiant floor heating system with HGSHP (RFHS-HGSHP, Case 2), the incorporation of PCM in the floor of Case 1 improves the indoor temperature stability by 8.6 %, enhances the load flexibility by 18.1 %, and reduces the total operating costs by 13.8 %. Furthermore, relative to the PCM floor with air-to-water heat pump system (PCM floor-AWHP, Case 3), the proposed system which substitutes the AWHP with the HGSHP, extends the duration of indoor comfort temperatures by 9.6 % and decreases the system electricity consumption by 33.7 %. The proposed system demonstrates excellent thermal and energy performance across all selected cities, proving to be both technically feasible and significantly beneficial for maintaining indoor comfort temperatures and reducing operational costs. This study offers valuable insights and technical support for designing and optimizing residential heating systems in cold regions, thereby promoting the development and application of renewable energy sources.

Optimizing sustainable energy integration: A novel approach using concentrated solar plant and hybrid power supply

This study pioneers sustainable energy solutions amidst escalating demand and fossil fuel depletion. Its primary novelty lies in proposing an integrated energy system, encompassing a concentrated solar plant, thermal energy system, and hybrid power supply within the solar energy domain. This integration substantially enhances overall system performance. The architectural design integrates various energy conversion components, including wind turbines, energy storage devices, and electric heaters, fostering synergistic collaboration. The optimization of energy usage is a key focus, utilizing heating and electricity costs as pivotal signals for participation in an Integrated Demand Response program (IRD). The demand response model, encompassing both thermal and electric loads, incorporates a sophisticated price elasticity matrix. Addressing challenges associated with renewable energy intermittency, phased carbon emissions, and the advantages of demand response, this study introduces an advanced system operation optimization model to effectively curtail operational costs. In the realm of methodology, this research breaks new ground by proposing an enhanced optimization algorithm that ingeniously combines the Whale Optimization Algorithm (WOA) with the Wavelet Transform (WT). This novel amalgamation significantly enhances the algorithm's search capabilities, providing superior performance in tackling the complexities inherent in the optimization problem. The proposed model undergoes rigorous evaluation in a comprehensive study featuring diverse energy sources and multiple scenarios. The outcomes of this evaluation distinctly showcase substantial reductions in overall operational costs compared to conventional approaches. This multifaceted contribution positions the study at the forefront of endeavors to revolutionize energy systems for enhanced sustainability and efficiency.

Economy and energy flexibility optimization of the photovoltaic heat pump system with thermal energy storage

Photovoltaic systems have been widely applied in building energy systems. However, with the sharp increase in demand of electricity in buildings during heating and cooling seasons, the uncertainty of photovoltaic generation further exacerbates the peak-valley difference in electricity use. To improve the economy and energy flexibility of buildings in hot summer and cold winter zones of China, a rule-based operation strategy was proposed for the photovoltaic heat pump with thermal energy storage system to optimize the size of the thermal energy storage system and system operation, with aims of minimizing the total annual cost of the system and maximizing the self-consumption rate of the photovoltaic generation. The effects of grid export power limits, grid import power limits, feed-in tariffs, and PV generation on the system operation optimization results were also investigated. The results indicated that by integrating the thermal energy storage system into the photovoltaic heat pump system, the self-consumption rate of the photovoltaic generation was reduced by 2.39 %, the total annual cost of the system was decreased by 6.61 %, and the payback period of the thermal energy storage system was 1.31 years. The optimum size of the thermal energy storage system and the self-consumption rate of the photovoltaic generation decreased with the increasing grid export power limits and grid import power limits. In contrast, higher grid export power limits, grid import power limits, feed-in tariffs, and PV generation all resulted in a lower total annual cost. This study provided a systematic design and operation optimization method for building energy systems.

Optimal sizing and operation of a hybrid energy systems via response surface methodology (RSM)

Hybrid energy systems (HESs) are the most important sources of energy demand-supply, have developed significantly around the world. Microgrids, renewable energy sources, remote telecommunications stations, greenhouses, etc., are being considered as HESs applications. Optimal sizing of these systems is considered as one of the important issues related to energy management. In this paper, the Response Surface Methodology (RSM) is proposed for the optimal sizing of a Photovoltaic (PV) system in a HESs. The suggested procedure solves the optimization problem by considering the factors affecting PV output power about the environmental conditions of the HESs. Providing a mathematical model for each of the input parameters and the ability to assessment the sensitivity of each of the input variables are the most important advantages of the proposed technique. In this paper, the RSM provides the most optimal sizing related to the PV system by considering climatic and geographical factors in the study site, and technical and economic issues related to the HESs. The optimal model obtained is evaluated by the Analysis of Variance (ANOVA) evaluation method, which is one of the important techniques of statistical evaluation. It should be noted that the RSM technique can be utilized to optimize all components of any HES.

Large-scale hydropower dispatching system based on cloud platform and its key technologies

In the past decade, the rapid expansion of hydropower capacity in China brings challenges to hydropower dispatching in data management and operation scheduling. In data management, the scale of hydropower operation data increases explosively, presenting typical big data features. In operation scheduling, the optimization of hydropower system operations is a high-dimension, nonconvex, and nonlinear problem. Conventional optimization methods encounter the “dimensionality disaster”, and cannot solve the scheduling problem efficiently. To deal with these challenges, by analyzing the data characteristics and operation scheduling requirements, this paper develops a large-scale hydropower dispatching system based on the cloud platform. The dispatching system consists of four layers: data access layer, cloud platform layer, data sharing layer, and application layer, which has good scalability and flexibility in data management and operation scheduling. In the application layer, key technologies based on the big data and cloud computing resources of the dispatching system are proposed to carry out the optimal operation of hydropower systems, including distributed parallel dynamic programming (DPDP) for long-/medium-term scheduling and data mining-based method (DMM) for short-term scheduling. Finally, the dispatching system is applied to the operation of hydropower systems in southwest China. Results indicate that: 1) The developed dispatching system is capable of processing 18 million data records per day, with a peak processing speed of 10,000 data requests per second, fulfilling the requirements for hydropower dispatching. 2) In long-term generation scheduling, under varying levels of runoff and plant scales, DPDP has demonstrated a remarkable reduction in computing time compared to dynamic programming, obtaining the same optimal solution with over 90 % less computational time. 3) In short-term peak shaving operation, on typical dry and wet days, the computing time of DMM is 7.2 s and 7.5 s, respectively, far less than the time of mixed-integer linear programming, and the maximum residual load obtained by DMM is lower than historical observations, indicating its effectiveness.

Enhancing Distribution Networks with Optimal BESS Sitting and Operation: A Weekly Horizon Optimization Approach

The optimal sitting and operation of Battery Energy Storage Systems (BESS) plays a key role in energy transition and sustainability. This paper presents an optimization framework based on a Multi-period Optimal Power Flow (MOPF) for the optimal sitting and operation of BESS alongside PV in active distribution grids. The model was implemented in AMPL (A Mathematical Programming Language) and solved using the Knitro solver to minimize power losses over one week, divided into hourly intervals. To demonstrate the applicability of the proposed model, various analyses were conducted on a benchmark 33-bus distribution network considering 1, 2 and 3 BESS. Along with the reduction in power losses of up to 17.95%, 26% and 29%, respectively. In all cases, there was an improvement in the voltage profile and a more uniform generation curve at the substation. An additional study showed that operating over a one-week horizon results in an energy gain of 1.08 MWh per day compared to single daily operations. The findings suggest that the proposed model for optimal sitting and operation of BESS in the presence of Renewable Energy Sources (RES) applies to real-world scenarios.

Transition to a 100 % renewable power supply in galapagos islands: Long-term and short-term analysis for optimal operation and sizing of grid upgrades

Owing to their remarkable biodiversity and endemic ecosystems, the Galapagos Islands are one of the most important World Heritage sites. However, due to their isolation and remote geographical location, around 83.6 % of the energy supply in Galapagos relies on the use of fossil fuels, which are polluting the air, the land and the sea, therefore requiring an adequate energy planning for the efficient transition towards a more sustainable and environmentally friendly energy sources. Although there were several previous studies of the decarbonization of Galapagos’ energy supply, there is a need for a more comprehensive approach that will combine planning (long-term) and operational (short-term) analysis to evaluate both, the future investment plans and performance of evolving energy supply system, as well as to provide an in-depth evaluation of required system upgrades in terms of locally available renewable energy resources. In order to fill this gap, this paper presents results of two different studies for the optimal decarbonization and transition to a 100 % renewable energy supply of the Galapagos Islands, giving details on the development of modelling tools required for the analysis of different scenarios and various techno-economic constraints. The first study is used for a long-term for analysing and strategic planning of the energy matrix until 2050, starting from existing power plants and following projections for the deployment of new renewable-based energy generation and dedicated energy storage sources to determine the optimal sizing and timing of required grid upgrades. The second study is a short-term annual performance analysis, which considers days of minimum, average and maximum available renewable energy resources to optimize system operation with existing and additional renewable energy generation and storage sources that will minimize, or keep at a certain target level, contributions from diesel generation. The two presented studies complement each other, allowing to evaluate transition to a 100 % renewable energy supply in Galapagos from both the investment/planning perspective and from the operational point of view. The results of two studies are very close in terms of the sizes of required new PV generation and associated energy storage in Baltra-Santa Cruz islands by 2050 (around 50 MW of PV and 145 MW of storage), with the short-term study allowing to identify curtailed PV generation (on average around 140 MWh per day) as an opportunity to connect new controllable loads, and with the long-term study allowing to evaluate interim targets for decarbonization pathways to Net Zero by 2050 through the related techno-economic optimization.

Optimized wastewater management utilizing multivariate statistical analysis: a case study of the Mascara wastewater treatment plant, Algeria.

Effective wastewater management is crucial in regions experiencing water scarcity and environmental stressors, such as pollution and climate change. Optimizing treatment processes is essential for achieving environmental sustainability. This study aims to highlight the importance of effective wastewater management strategies, particularly in regions facing water scarcity. Our objective was to identify key factors influencing the treatment process. Therefore, we evaluated associations between physicochemical parameters using multivariate statistical methods, including Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Our findings categorize the monthly water samples into three distinct groups based on levels of organic pollution: the first group (July, August, and September) is characterized by high oxygenation levels and significantly low organic pollution, indicating optimal system operation. The second group (April, October, November, and December) exhibits low oxygenation and low organic pollution, promoting sludge settling and pollutant reduction. The third group (January, February, March, May, and June) shows significantly high organic pollution and low oxygenation, which corresponds to unfavorable environmental conditions. Our study demonstrates the effectiveness of multivariate statistical methods in optimizing wastewater treatment processes, providing crucial insights for environmental sustainability and water resource management.

Optimal Design and Operation of Electrolytic Hydrogen Production and Storage System for Solar Photovoltaic Power Smoothing

Even though the increasing penetration of solar photovoltaic (PV) energy into the electric grid can reduce the carbon emission of power generation, the intermittency and variability of renewable solar energy lead to frequent and steep ramping operations of conventional fossil fuel power plants. Hydrogen production via water electrolysis using solar power can serve as a controllable load and provide a short/long duration energy storage to mitigate the grid fluctuations (i.e., PV smoothing) and improve the resiliency of power grid. The generated green hydrogen will be further stored under high pressure for subsequent utilizations (e.g., hydrogen fuel cell electric vehicle refueling).Polymer electrolyte membrane (PEM) electrolyzer with high efficiency and quick dynamic response is used to generate hydrogen [1,2]. The capacity factors of PV field and PEM electrolyzer can affect the performance of PV smoothing on the cloudy day. Even though PEM electrolyzers directly producing high pressure hydrogen are more energy efficient than ambient pressure electrolyzers followed by mechanical compression, higher pressure leads to more undesired hydrogen back diffusion to the oxygen side, which causes lower hydrogen production efficiency, cell degradation, and safety risk of hydrogen explosion limit [3,4]. Optimal design and operation of electrolyzer under fluctuating solar power are formulated based on an integrated hydrogen-based energy storage system (renewable solar coupled with green hydrogen production and storage). Different PV smoothing scenarios as well as optimal size and operation conditions of the PEM electrolyzer are investigated.The high-fidelity dynamic model of a PEM electrolyzer cell/stack is established with consideration of two-dimensional (“through-plane” and “in-plane”) mass/heat transfer coupled with electrochemical kinetics. The electrochemical reactions are considered using Butler-Volmer kinetics with varying surface molar concentrations of components. Maxwell-Stefan diffusion equation is adopted to calculate the gas-phase species molar concentration in the backing and catalyst layers. Hydrogen permeation back through membrane is included with consideration of both diffusion and convective transports as a function of operational pressure.To evaluate the transient response of the PEM electrolyzer stack, real-time PV data based on an Orlando Utilities Commission (OUC) solar farm is smoothed using the developed power signal control algorithm. The optimal PV smoothing control algorithm and the electrochemical dynamic stack model show the effectiveness of hydrogen-based energy storage system in smoothing the PV signal to improve the grid stability and flexibility.[1] Kumar, S.S. and Himabindu, V., 2019. Hydrogen production by PEM water electrolysis–A review. Materials Science for Energy Technologies, 2(3), pp.442-454.[2] Götz, M., Lefebvre, J., Mörs, F., Koch, A.M., Graf, F., Bajohr, S., Reimert, R. and Kolb, T., 2016. Renewable Power-to-Gas: A technological and economic review. Renewable energy, 85, pp.1371-1390.[3] Trinke, P., Bensmann, B., Reichstein, S., Hanke-Rauschenbach, R. and Sundmacher, K., 2016. Hydrogen permeation in PEM electrolyzer cells operated at asymmetric pressure conditions. Journal of The Electrochemical Society, 163(11), p.F3164.[4] Scheepers, F., Stähler, M., Stähler, A., Rauls, E., Müller, M., Carmo, M. and Lehnert, W., 2020. Improving the efficiency of PEM electrolyzers through membrane-specific pressure optimization. Energies, 13(3), p.612.

Experimental investigation on wireless integrated smart system for energy and water resource management in Indian smart cities

In the realm of India's smart cities, precise meter readings are essential for effectively managing domestic energy and water systems. Nevertheless, meter reading employing conventional techniques prove to be both costly and time-consuming, especially considering the vast user base and deficit of everyday consumption analysis. To cope with this challenge, the proposed solution introduces a novel unified wireless smart metering system for measuring energy and water usage. This prototype harnesses the smart, advanced metering technology to manage energy and water resources. The presence of data and remote control capabilities in smart distribution transformers offers distribution operators the chance to optimize system operation and control. The innovative system proposed utilizes smart metering technology and incorporates a distribution system self-healing mechanism against power outages, revolutionizing the way utilities manage water and energy consumption. The system not only offers real-time consumption statistics to the utility provider but also provides the flexibility to remotely control the system's turn-on and turn-off functions. The system effectively captures real-time data, transmits it via LoRa to Telegram application. The system automatically re-establishes remote connections in the event of disconnections caused by emergency conditions or non-payment of the bill, once the relevant issue has been resolved. Additionally, the system promptly notifies authorities on overload, over-temperature, and instances of electricity theft. The system proficiently generates bills based on the consumed data and seamlessly transmits the consumed units to the authorities. The innovative system proposed has proven its ability to communicate effectively, yielding results that align with the given circumstances.

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

Mentor Management System

Abstract: This paper introduces the "Mentor Management System," an innovative Java-based Database Management System (DBMS) web application designed to enhance mentor-mentee interactions and administrative oversight in academic institutions. Addressing the growing demand for efficient mentorship collaborations, the system aims to streamline connections between mentors and mentees while optimizing the overall mentorship process for students, mentors, and administrative personnel. The Online Student Mentoring System offers a comprehensive platform facilitating seamless interactions among administrators, mentors, and students. Administrators manage and optimize system operations, ensuring efficiency across all levels. Equipped with specialized tools, mentors engage with students to understand their unique needs, providing personalized guidance that fosters both academic progress and personal development. Students benefit from a user-friendly interface, allowing effortless access to mentorship, query resolution, and tailored insights to enrich their educational experience

Optimal Operation of the Multi-reservoir Rainwater Harvesting System for Hydropower Generation

Optimal Operation of the Multi-reservoir Rainwater Harvesting System for Hydropower Generation

Process Modeling and Optimization of Supercritical Carbon Dioxide-Enhanced Geothermal Systems in Poland

This paper presents a comprehensive analysis of supercritical carbon dioxide (sCO2)-enhanced geothermal systems (EGSs) in Poland, focusing on their energetic performance through process modeling and optimization. EGSs harness the potential of geothermal energy by utilizing supercritical carbon dioxide as the working fluid, offering promising avenues for sustainable power generation. This study investigates two distinct configurations of sCO2-EGS: one dedicated to power generation via a binary system with an organic Rankine cycle and the other for combined power and heat production through a direct sCO2 cycle. Through accurate process modeling and simulation, key parameters influencing system efficiency and performance are identified and optimized. The analysis integrates thermodynamic principles with geological and operational constraints specific to the Polish context. The results highlight the potential of sCO2-EGSs to contribute to the country’s energy transition, offering insights into the optimal design and operation of such systems for maximizing both power and thermal output while ensuring economic viability and environmental sustainability.

Penggunaan Energi Terbarukan PLTS untuk Efisiensi Biaya dan Keberlanjutan Usaha Bubur Bayi Shashi di Bukittinggi

Shashi Baby Porridge business in Bukittinggi faces significant operational cost challenges due to high electricity consumption, particularly from production equipment. To address this issue, a community service team from the Department of Electrical Engineering at Padang State Polytechnic implemented a Solar Power System (SPS) as an alternative solution. The service process included energy needs analysis, SPS system design, installation, and performance monitoring and evaluation. The implementation of the SPS successfully reduced electricity costs and dependence on conventional electricity while supporting environmental conservation efforts. The installed SPS, with a total capacity of 200 Wp, provides energy for lighting, resulting in significant savings in operational costs. Socialization with the owners and workers was also carried out to ensure optimal system maintenance and operation. The outcomes of this project are expected to inspire other small businesses to adopt renewable energy technologies to enhance efficiency and sustainability.

APPLICATION OF THE METHOD OF GROUP ARGUMENTS ACCOUNTING FOR BUILDING MODELS OF SHORT-TERM FORECASTING OF GAS CONSUMPTION BY THE POPULATION

Problem statement. One of the most important tasks in optimizing the operation modes of urban gas distribution systems is to develop a short-term strategy for such modes. Processing of planned and statistical indicators of gas consumption modes is one of the key methods for ensuring the optimal functioning and normal operation of gas supply systems. The most promising modeling method for solving forecasting problems is the method of self-organization of models implemented as the method of group arguments accounting (MGАA). Short-term forecasting of gas consumption is the main source of information for decision-making in the process of planning consumption regimes and operational dispatch control. The MGАA method, also known as the Ivakhnenko method, is a powerful tool for obtaining highly accurate forecasting models and is gaining importance for forecasting in energy resource management and planning their use. The solution to such a problem is relevant, as it makes it possible to analyze the volume of natural gas consumption by the population for the future period. The purpose of the article is to build mathematical models for forecasting gas consumption using the method of group arguments accounting based on input data. Conclusion. To select the best models the following parameters are recommended: tomorrow's gas consumption as a function (main criterion) and arguments (additional criterion) - gas consumption to date, air temperature to date, yesterday's temperature and temperatures for the past 3, 5, 7 days, wind speed and day of the week. The resulting mathematical models of natural gas consumption by the population can be used to analyze the volume of natural gas consumption. The method allows us to find a single optimal mathematical model that best solves the problem.

Modeling sorption-enhanced methane synthesis for system control and operation

To address energy transition in the immediate future, synthetic energy carriers are among the most promising options for decarbonization due to their capability to store renewable energy in the long term and rely on existing infrastructure. Methane, as a substitute for natural gas, can be produced at high purity through sorption-enhanced processes.These processes are discontinuous, they require sorbent regeneration once saturation is reached. Therefore, optimal operation of these complex systems requires the control of many time-dependent variables. In this framework, the rapid analyses that can be carried out via dynamic mathematical models could be greatly beneficial in lowering costs at the design stage.In this study, a model of a sorption-enhanced methanation system was developed and validated with experimental data. The proposed model allows the dynamic behavior of the process to be captured during production and sorbent regeneration. Insights into the process show the behavior of the sorbent bed while retaining water, demonstrating how the bed saturation occurs and how to effectively exploit it.The model potential was assessed by analyzing the alternating operation of two reactors for continuous production. The importance of bed drying was underlined to ensure the enhancement of the reaction and maximize the conversion.

Optimal operation of multi-carrier energy systems considering demand response: A hybrid scenario-based/IGDT uncertainty method

The optimal operation of multi-carrier energy systems (MCESs) has opened new horizons for energy network management and the satisfaction of consumers. In this paper, the optimization of the MCES's operation cost is considered by combining several energy hubs (EHs). To make optimal use of thermal and electrical demand response programs (TDRPs and EDRPs), the cost factors are extracted using a new sensitivity analysis (SA) method. Then, taking into account the optimized cost coefficients in EDRP and TDRP, the optimal operation of MCES is investigated in the presence of uncertain parameters. In this paper, a two-stage uncertainty method is used for solving the uncertain problem. First, the uncertainty of wind, photovoltaic (PV), and electrical and thermal loads is modeled with a scenario-based method, in the second step, the price uncertainty of generated electricity is added to the previous model using the information gap decision theory (IGDT). A hybrid scenario-based/IGDT uncertainty has been performed simultaneously at the GAMS platform. The results show the impact of each uncertainty parameter and system resilience due to the optimization done in the problem and the EDRP and TDRP programs. The results show about 135$ difference between the lowest and highest scenarios at a total operation cost.

Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

An Electrolyzer Model for Power System Operation Optimization Over Broad Temperature Range

An Electrolyzer Model for Power System Operation Optimization Over Broad Temperature Range