Search

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting. [ABSTRACT FROM AUTHOR]

A large volume of solar energy dissemination in a supply grid originates extreme variations in the load, resulting in a duck-form load arc that can cause stability issues. Also, the cost of energy consumption is found to vary between the off-peak and peak loads observed in the duck-shaped load curve. Accurate load forecast and demand response program is a key task for duck curve management in a distribution structure. Hence, this work proposes a Demand Response (DR) program using deep learning neural networks namely Long Short-Term Memory (LSTM). The proposed DR program is implemented in a modified 12 bus radial distribution network for duck curve management, where voltage stability is taken care of simultaneously minimizing the electricity cost in an energetic pricing environment. LSTM is used for forecasting the load and linear programming is used for load shedding. Therefore, this paper resolves the dual aims of flattening the duck-shaped load arc and minimizing electricity costs by combining them into a single objective function. [ABSTRACT FROM AUTHOR]

The charge/discharge operation of the prosumer's energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG's operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers' complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member's energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved. [ABSTRACT FROM AUTHOR]

An electromechanical method is proposed to avoid peak dynamic loads in a steady state, the condition of smooth start and emergency stop, as well as correction of the scraper conveyor productivity depending on the load flow level of the blasting combine, which will increase the reliability and reduce the energy and metal consumption of the transport link of the mining complex. [ABSTRACT FROM AUTHOR]

This paper begins by investigating a power quality problem in the case study area using monthly energy consumption data, the peak and minimum load of the substation, a fault summary, and the RMS voltage profile of the case study area. The content of the paper comprises three parts: the first involves MATLAB design and simulation of D-STATCOM with a fuzzy logic controller for the selected case study area. The second part entails MATLAB design and simulation of the DVR with a fuzzy logic controller. The final part presents a hybrid of the DVR and the D-STATCOM back-to-back using a DC link capacitor to implement a more recent and effective power quality improvement technique, namely UPQC. In the case of a three-phase fault, the voltage sag in the case study area is 0.6PU, with the remaining 0.4PU drop-down voltage requiring injection using the proposed FACT device for compensation. All proposed power quality problem mitigation techniques inject the required voltage and compensate for the voltage sag in the substation. For instance, D-STATCOM compensates the RMS voltage from 9KV to 14KV, DVR compensates the RMS voltage from 9KV to 14.7 KV, and UPQC compensates the value to 14.9KV. Due to the three-phase fault, the rise in reactive power of the existing outgoing feeder is 13MW. This unexpected rise in reactive power is reduced to 2.6 MW using D-STATCOM, DVR reduces it to 1.2 MW, and UPQC reduces the value below 1MW. The existing power factor of the case study area without any mitigation technique is 0.68. All proposed techniques improve the existing power factor to a more acceptable value. For instance, D-STATCOM improves the power factor from 0.68 to 0.83, DVR improves the value to 0.88, and UPQC, the more accurate power quality mitigation technique, improves the power factor to 0.93. Finally, the results are compared based on the simulation results. [ABSTRACT FROM AUTHOR]

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users, the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies. For this purpose, a power grid-flexible load bilevel model is constructed based on dynamic pricing, where the leader is the dispatching center and the lower-level flexible load acts as the follower. Initially, an upper-level day-ahead dispatching model for the power grid is established, considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints. Then, the lower level comprehensively considers the load characteristics of industrial load, energy storage, and data centers, and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function. Finally, the proposed method is validated using the IEEE-118 system, and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively, thereby reducing the peak-valley difference and decreasing users' purchasing costs. [ABSTRACT FROM AUTHOR]

The enhancement of energy efficiency in a distribution network can be attained through the adding of energy storage systems (ESSs). The strategic placement and appropriate sizing of these systems have the potential to significantly enhance the overall performance of the network. An appropriately dimensioned and strategically located energy storage system has the potential to effectively address peak energy demand, optimize the addition of renewable and distributed energy sources, assist in managing the power quality and reduce the expenses associated with expanding distribution networks. This study proposes an efficient approach utilizing the Dandelion Optimizer (DO) to find the optimal placement and sizing of ESSs in a distribution network. The goal is to reduce the overall annual cost of the system, which includes expenses related to power losses, voltage deviation, and peak load damand. The methods outlined in this study is implemented on the IEEE 33 bus distribution system. The outcomes obtained from the proposed DO are contrasted with those of the original system so as to illustrate the impact of ESSs location on both the overall cost and voltage profile. Furthermore, a comparison is made between the outcomes of the Ant Lion Optimizer (ALO) and the intended Design of Experiment DO, revealing that the DO has obtained greater savings in comparison to the ALO. The recommended methodology's simplicity and efficacy in resolving the researched optimization issue make the acquired locations and sizes of ESSs favorable for implementation within the system. [ABSTRACT FROM AUTHOR]

We study the Non-preemptive Peak Demand Minimization (NPDM) problem, where we are given a set of jobs, specified by their processing times and energy requirements. The goal is to schedule all jobs within a fixed time period such that the peak load (the maximum total energy requirement at any time) is minimized. This problem has recently received significant attention due to its relevance in smart-grids. Theoretically, the problem is related to the classical strip packing problem (SP). In SP, a given set of axis-aligned rectangles must be packed into a fixed-width strip, such that the height of the strip is minimized. NPDM can be modeled as strip packing with slicing and stacking constraint: each rectangle may be cut vertically into multiple slices and the slices may be packed into the strip as individual pieces. The stacking constraint forbids solutions where two slices of the same rectangle are intersected by the same vertical line. Non-preemption enforces the slices to be placed in contiguous horizontal locations (but may be placed at different vertical locations). We obtain a (5 / 3 + ε) -approximation algorithm for the problem. We also provide an asymptotic efficient polynomial-time approximation scheme (AEPTAS) which generates a schedule for almost all jobs with energy consumption (1 + ε) OPT . The remaining jobs fit into a thin container of height 1. This AEPTAS is used as a subroutine to acquire the (5 / 3 + ε) -approximation algorithm. The previous best result for NPDM was a 2.7-approximation based on FFDH (Ranjan et al., in: 2015 IEEE symposium on computers and communication (ISCC), pp 758–763, IEEE, 2015). One of our key ideas is providing several new lower bounds on the optimal solution of a geometric packing, which could be useful in other related problems. These lower bounds help us to obtain approximative solutions based on Steinberg's algorithm in many cases. In addition, we show how to split schedules generated by the AEPTAS into few segments and to rearrange the corresponding jobs to insert the thin container mentioned above, such that it does not exceed the bound of (5 / 3 + ε) OPT . [ABSTRACT FROM AUTHOR]

In the Energy Internet, users no longer have a single demand response for electricity, but an integrated demand response for multiple types of energy. In integrated demand response, users participate in demand response directly by switching energy sources without having to change their energy usage time habits. A mathematical model of the electricity-heat-gas energy circuit, based on the correlation between different energy sources, considering the combined demand response economy and comfort of the household user. Real-time dispatch optimization modeling with the objective of real-time peak shaving within a day and tariff regulation as a means to provide a decision basis for real-time peak shaving and smoothing of the electricity load curve. An example is given to verify the effectiveness of the model and the feasibility of the solution method, at the peak of electricity consumption around 8 p.m., through real-time dispatching, the peak electricity load decreases by about 2 kWh, and the gas consumption increases slightly. And the economic basis of integrated demand response scheme decision-making is analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Riwayat: Educational Journal of History & Humanities is the property of Riwayat: Educational Journal of History & Humanities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of cold thermal energy storage systems (CTES) is to reduce power consumption in air conditioning systems. For the optimization, the objective functions are considered as exergy efficiency and total annual cost owing to CO2 emissions from the CTES systems. Multi-objective optimization methods are used by MOPSO and SEAP2. The findings achieved from multi-objective optimization indicated a difference in the optimal amounts of design points compared to single-objective optimization, the first objective function (i.e., exergy efficiency), and the second objective function (i.e., total annual costs). In addition, the results of revising this model represent that because of the use of CTES, there is a reduction of electricity consumption in the system. Also, because of transferring cooling load from peak hours to low consumption and reducing power consumption, the system saw a reduction in operating costs compared to a traditional air conditioning system. Finally, the results show that the payback period for a CTES system in partial storage mode is 3.43 years and for a full storage mode is near 3.88 years. Moreover, due to further reduction of operating charges in full storage mode, the total stored cost of this system is more than the partial storage type. It should be noted that the use of the CTES system decreases the production of CO2, which reduces environmental pollution. Finally, the PCMs used in the construction industry are introduced and compared with each other in terms of exergy efficiencies. [ABSTRACT FROM AUTHOR]

The integration of a CSP tower system with a PV solar field, sharing a thermal energy storage, is modeled and discussed. The tower system uses a new-design solid particle fluidized bed receiver integrated with a thermal storage, where hot particles are directly collected to store daily energy for overnight production of electricity. The PV solar field is aimed to supply the daily energy demand; when there is a surplus of PV energy production, the electric energy is converted to heat and accumulated in the thermal storage too. The integration of the two energy systems is modeled, building efficiency functions for all the sub-components of the integrated plant (heliostat field, receiver, storage, power block, PV field). Yearly simulations are performed for two different locations, Spain and Australia, obtaining that a system with a peak power of 10 MWe CSP + 15 MWe PV can supply—with a limited curtailment—a fraction of more than 60% (respectively, 62% and 68%) of a realistic electric load with a peak demand around 10 MW, to be compared with the 45/47% of the same load obtained adopting a PV-only system with the same overall peak power. In the integrated system, PV directly supplies 40/41% of the load, the remaining 23/28% being produced by the power block (mainly fed by the CSP). [ABSTRACT FROM AUTHOR]

Demand side management (DSM) is a smart grid technology that enables consumers to make decisions about their energy use, lowers energy suppliers' peak hour demand, and changes the load profile. Demand Side Management (DSM) is regarded as the most significant method used in a Smart Grid (SG), as it helps consumers produce accurate information about their electrical energy usage and assists the utility in reducing peak load demand and reshaping the demand curve. By effectively utilising storage with Renewable Energy Systems (RES), DSM seeks to reduce peak demand, electricity costs, and emission rates. In this paper, we have proposed a load-shifting method for the DSM with a large number of controllable devices. The load-shifting issue has been handled hourly, throughout the course of a 24-hour day, in order to reduce the peak demand, lower the power cost, and minimise the Peak to Average load Ratio (PAR). The Archimedes Optimization (AO) method has been utilised in residential loads in SG to achieve the goal of load shifting by minimising of the problem to the DSM. The simulation findings demonstrate that the suggested demand side management technique generates significant cost savings while lowering the smart grid's peak load demand. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Full-scale model tests of three integral prestressed prefabricated frame structure system (IMS) prestressed friction joint specimens under low cyclic loading were conducted to explore the factors influencing the hysteretic behavior of a new joint. The use of a high-strength grouting material (steel fiber) for joints was found to delay the damage of the grouting material under repeated loads and cause reduction of effective prestress. Additionally, replacing the prestressed reinforcement with unbonded prestresssed reinforcement in the precast column and the open channel near the column maximized the deformation capacity of the structure and reduced the structural damage and stiffness degradation in later stages. With an increase in the length of the unbonded section, the peak load of the specimen decreased and the energy consumption was worsened. To enhance the energy consumption of the joint, this study proposes a hybrid connection joint between the energy-consuming and prestressed reinforcements in the open slot. Using ABAQUS numerical modeling and analysis, the joint structure was further optimized and the variations in the energy consumption capacity of the hybrid joint with the position of the prestressed reinforcement, the proportions for the local weakening of energy-dissipating reinforcement, and the bonding mode were obtained. The numerical results demonstrated that the seismic performance index of the hybrid joint was comparable to or even higher than that of the cast-in-situ joint and indicated excellent self-centering and damage recover capacity. [ABSTRACT FROM AUTHOR]

Solar power on Earth is characterized by its intermittent nature, limiting its practical application to peak loads only. However, this limitation can be overcome by implementing a concept known as Satellite Solar Power Station (SSPS), which involves deploying solar panels in space. Nevertheless, this approach poses various challenges that need to be addressed. One of the primary obstacles lies in determining the feasibility of this plan. This research emphasizes the importance of exploring economically viable methods for establishing SSPS in space, utilizing a Reusable Launch Vehicle and Hohmann's Transfer technique. To support these proposals, optimization techniques involving multi-stage impulsive maneuvers are employed. Moreover, a comprehensive load dispatch algorithm is mathematically derived and developed to adapt to the changing demands on Earth, achieving a balance between load requirements and antenna size. In addition, discussions are conducted regarding the potential implementation of frequency reconfigurable Microwave systems for future applications. The overall objective of this study revolves around the future deployment and efficient load dispatch from SSPS to meet the energy demands of base loads on Earth, thereby making solar power a viable option. [ABSTRACT FROM AUTHOR]

Cool paint (CP) is effective to decrease cooling energy consumption, but it brings heating penalty. Phase change material (PCM) can alleviate the heating penalty caused by CP. In this work, eight cities with different climates are analyzed. According to cooling/heating energy consumption, these cities are divided into different categories. Compared with CP roof, adding PCM on the CP has little effect for further reducing the cooling energy use or peak load. CP increases heating energy consumption, but does not aggravate the peak heating load range. The higher the proportion of heating energy consumption increased by CP, the more PCM amount needs to compensate for the heating penalty. When increasing PCM thickness, the peak heating load decreases first and then unchanged. For the total energy consumption, CP + PCM is always superior to CP. Applying CP alone is only preferable in cities whose heating energy consumption ratio does not exceed 0.66. In other cities, applying 10–20 mm PCM on the basis of CP can realize positive energy saving benefits, which broadens the applicable climate for CP. This work provides valuable guides for the application of CP alone, and the optimization effect of PCM on CP under different climates. [ABSTRACT FROM AUTHOR]

With the increasing global demand for sustainable energy sources and the intermittent nature of renewable energy generation, effective energy storage systems have become essential for grid stability and reliability. This paper presents a comprehensive review of pumped hydro storage (PHS) systems, a proven and mature technology that has garnered significant interest in recent years. The study covers the fundamental principles, design considerations, and various configurations of PHS systems, including open-loop, closed-loop, and hybrid designs. Furthermore, the review highlights the crucial role of PHS systems in integrating renewable energy sources, mitigating peak load demands, and enhancing grid stability. An in-depth analysis of current and emerging trends, technical challenges, environmental impacts, and cost-effectiveness is also provided to identify potential areas for future research and development. The paper concludes by offering a perspective on the challenges and opportunities that PHS systems present, underlining their potential to significantly contribute to a sustainable and reliable energy future. [ABSTRACT FROM AUTHOR]

This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid. [ABSTRACT FROM AUTHOR]

For a certain functional building group, the coupling relationship between the load of a single unit and entire building groups lies in the spatial and temporal patterns personnel distribution. Using the DeST (Designer's Simulation Toolkit) method, the personnel flow trajectory in a university campus in Shanghai was analyzed and the established energy consumption calculation model based on personnel flow trajectory of university buildings. It is proposed that personnel mobility should be included in the application of DeST to predict the total load of building groups. The results show that after the people flow trajectory was included, the total annual accumulated load decreased from 27.01 to 19.39 GWh, reducing by 28.20%; the hourly load fluctuation became more stable, the daily peak load decreased by 22.57%, and the daily peak valley difference decreased by 43.74%. The results suggest that the prediction for the air conditioning load of building complexes would be better when the trajectory of human flow is considered, minimizing unnecessary system scale and investments. Under the "double carbon" policy, the personnel flow trajectory model can effectively reduce waste in air conditioning energy consumption in campus buildings. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research of influencing factors on the electricity consumption of electric buses Higer KLQ6125GEV3 in real working conditions is the result of experimental measurements conducted on the EKO 1 line in Belgrade over a long period which included typical seasonal periods (winter, summer, transitional) of electric bus operation as well as characteristic periods of work during the day (peak load, intermediate period, first/last departures). The results of this research are the basis for reviewing and ranking the influential factors on the electricity consumption of electric buses that are of great importance in planning operations of the line, analysis of energy efficiency and operating conditions. [ABSTRACT FROM AUTHOR]

Peak load shaving using energy storage systems has been the preferred approach to smooth the electricity load curve of consumers from different sectors around the world. These systems store energy during off-peak hours, releasing it for usage during high consumption periods. Most of the current solutions use solar energy as a power source and chemical batteries as energy storage elements. Despite the clear benefits of this strategy, the service life of the battery energy storage system (BESS) is a driving factor for economic feasibility. The present research work proposes the use of storage systems based on actively connected batteries with power electronics support. The proposed scheme allows the individualized control of the power flow, enabling the use of batteries with different ages, technologies or degradation states in a same BESS. The presented results show that overcoming inherent limitations found in passively connected battery banks makes it possible to extend the system's useful life and the total amount of dispatched energy by more than 50%. Experimental tests on a bench prototype with electronified batteries are carried out to proof the central concept of the proposed solution. Computational simulations using collected data from a photovoltaic plant support the conclusions and discussions on the achieved benefits. [ABSTRACT FROM AUTHOR]

In this article, energy optimization of the cooling system of IKEA Budaörs is carried out. The cooling system is served by a centrifugal water chiller and includes a large-volume cooling buffer tank. The facility operates the hydraulic system of the buffer storage tank only during the transitional period. The main goal is to reduce energy consumption by changing the operating strategy of the existing system. To test the operating strategies, the operation and the thermal load of the shopping center during the summer season had to be simulated to find the best operation strategy. A hybrid method (real data and calculated values) was used in the simulation. The three operating scenarios examined show that the annual energy consumption and the number of operating hours of the chiller can be reduced by using the buffer tank with the right strategy. In the examined scenarios, a 30% energy improvement was achieved. The possibility of using a buffer tank is significantly limited by the fact that the heat exchangers were sized for low forward water temperatures. By re-sizing the heat exchangers, the utilization of the buffer tank could be considerably improved in conditions close to peak heat load. [ABSTRACT FROM AUTHOR]

The electric power system is critical to supporting the economic growth of a country. On the other hand, the growing concern about the environment in recent years has pushed many countries to create strategies to minimize greenhouse gas emissions by increasing the proportion of new and renewable energy (NRE) sources. In Indonesia, the Java-Bali power grid is the most extensive electricity system, with a demand of 177,692.43 GWh with a peak load of 40,059.74 MW in 2019. However, the energy mix in Java-Bali is dominated by coal at 70%, followed by natural gas at 21.22%, renewable energy at 7.71%, and fuel at 0.14%. Therefore, there is an urgent need to increase the use of NRE sources in fulfilling the electrical energy demand, with the government setting a renewable energy utilization target of 23% in 2025 and 31% in 2050. This study aims to create a power-generating capacity development planning scenario with the best use of NRE sources at the lowest cost, to support the Indonesian policymakers in obtaining those targets. The Balmoral model with General Algebraic Modeling System (GAMS) programming was used to optimize the planning model for the power generating capacity. The results show that the development planning scenario with a total additional generating capacity of 15,035 MW is the scenario with the most optimal utilization of NRE sources and the lowest cost, with an estimated total investment cost of IDR 901 trillion. Furthermore, this scenario can increase the composition of renewable energy in the Java-Bali system to 16.95% in the next ten years. [ABSTRACT FROM AUTHOR]

This paper collects into a medical waste plasma gasification, a cleaning system, a gas turbine, a syngas storage tank and a coal-fired power unit to establish the medical waste plasma hybrid peak shaving system. In this system, medical waste is transported into a plasma gasification furnace and prepared as syngas by reaction. Then, the syngas is delivered to the gas turbine after cooling and deacidification. Finally, the flue gas from the combustion of the gas turbine is used to heat the condensate of a coal-fired power unit. Compared to the coal-fired power unit, the system has a larger range of peak load capability. Establish the integrated regional power grid dispatching model. The paper selects five typical days, which combine the actual data to comprehensively consider many factors. Then analyze the optimal economy of the regional power grid and propose the optimal dispatch scheme for the regional power grid. After modification, the system energy efficiency is 37.38 %, and system exergy efficiency is 36.19 %. After the dispatching optimization, the average daily profit is 140.78 k$, the profitability can reach initial cost in 6.20 years, and the net present value generated by the medical waste plasma hybrid peak shaving system is 178,410.43 k$. • A medical waste plasma gasification system is designed for peak load of power grid. • The system's feasibility, energy efficiency and exergy efficiency are discussed. • The paper selected typical days to discuss the feasibility and economy of system. [ABSTRACT FROM AUTHOR]

• Data analysis framework to examine institutional buildings' peak electrical demand. • Quantile-based metrics are introduced to comprehend peaking patterns in buildings. • Cross building ranking is established to identify electrically inefficient buildings. • Framework was implemented and tested in real world university settings. • Cloud-based dashboard was developed for visually intuitive display of results. Peak loads in higher education institutional building clusters (IBCs) possess considerable economic repercussions on their overall operations. Thus, identifying electrically inefficient buildings presents a significant opportunity to curtail peak loads and promote energy efficiency in IBCs. Existing literature implements clustering algorithms to comprehend the electrical demand dynamics of buildings to analyze disparity in their load behavior. These techniques perform well in single building environment, however, fall short in comprehending the demand dynamics for building clusters, specifically during peak loads. This study introduces a cloud-oriented quantile-based data analysis framework, specifically designed for simultaneously evaluating demand profiles of multiple buildings within IBCs. Quantile-based metrics namely, Value-at-Risk, Conditional Value-at-Risk and Conditional Value-at-Risk standard deviation are implemented to comprehend the electrical fluctuations and quantify the electrical impact of each building during campus-wide peak demands. A cross-building comparison is established by linearly ranking buildings following two key criteria: (i) buildings with frequent demand fluctuations and (ii) buildings exerting a high electrical impact on overall IBC demand. Both criteria are equally weighted while ranking to identify the most inefficient buildings during peak loads. The framework is implemented in a Canadian university and resulted a substantial 50 MW demand reduction through recommissioning and retrofitting of inefficient buildings. [ABSTRACT FROM AUTHOR]

In the smart grid, residential inverter air conditioners (AC) with significant demand response (DR) potential due to their load flexibility and as the major contributors to peak electricity, need to be grid-responsive to relieve power supply-demand imbalance and ensure thermal comfort. Model predictive control (MPC) has strong capabilities for unlocking the flexibility of residential buildings to realize DR by responding to electricity prices. However, the high computational requirements and complex control system integration processes make the application of MPC a significant challenge. A hierarchical nonlinear MPC (HNLMPC) is developed to realize grid-responsive control for residential inverter ACs by responding to real-time electricity price signals. The controller consists of three parts: the upper-level supervisor MPC, the lower-level optimal PID controller, and the signal converter. The indoor air temperature is selected as the optimized setpoint sequence passed from the upper level to the lower level. A nonlinear prediction model is developed considering the dynamic performances of the inverter AC and the coupled thermal response of an air-conditioned room. A test platform is constructed using Simulink and Simscape to access the DR performance of HNLMPC by comparing it with different rule-based control methods, hierarchical linear MPC, and centralized MPC. The control results show that HNLMPC can achieve peak load shifting and peak shaving without sacrificing thermal comfort by adjusting the room temperature to charge and discharge cooling for the building's thermal mass. Additionally, it enables plug-and-play capability for practical applications, reducing the dependency on local computing power and the need for accurate models. Compared to basic rule-based control, HNLMPC reduces peak-hour energy consumption by 31.6% and total electricity costs by 14.3% over the entire cooling season. • A hierarchical nonlinear MPC is developed for inverter air conditioners. • The prediction model considers the coupled response of an air-conditioned room. • The HNLMPC realizes plug-and-play and has strong robustness. • The total peak-hour energy saving is 31.6% for the cooling season. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading, whereby customers can trade energy with one another rather than the energy supplier only, has the potential to save money for consumers whilst also incentivising more efficient and environmentally beneficial behaviour. Many existing models for P2P only consider a real-time or hour-ahead market, which does not allow proper scope for the planning of flexible demand or for energy storage. Accordingly, in this model we employ a day-ahead continuous double auction (CDA), in which all the upcoming timeslots are simultaneously open for trading. This allows schedules for device dispatch to be developed properly. We consider the flexibility and interdependence of bidding across different timeslots and develop strategies to address this. Furthermore, we consider the trade of heat as well as power, via a low temperature heat network. Heat and power trading interact due to the use of air source heat pumps (ASHPs) as well as reversible solid oxide cells (rSOCs), which can provide combined heat and power, or alternatively produce hydrogen via water electrolysis. In our case study, the P2P market is simulated with 25 houses participating, for two week-long periods in different climate conditions. P2P electricity trading is found to bring a marked reduction in reliance on grid electricity, and a reduction in peak grid load. This is brought about mainly by the incentive for rSOCs to generate at a higher average load factor, and the average house makes savings of ca. £10 / week in winter weather. Heat trading brings a further decrease in reliance on grid electricity, and largely eliminates the use of inefficient resistive heat. However, the heat trading may not be financially worthwhile in all conditions. • Simulation of peer-to-peer (P2P) energy trading via a continuous double auction • Heat and power both considered, with fuel cells and heat pumps coupling these • Auction format allows flexible devices to plan schedules in advance • With electricity trading, higher load factor for fuel cells, cuts reliance on grid • Heat trading brings extra technical benefits though cost savings are not certain [ABSTRACT FROM AUTHOR]

Cloud computing is an effective computing methodology used in all stages of business. Most of the Cloud Data Centers (CDC) operates on the basis of peak load and huge scales. Hence, it necessitates saving the energy in CDC. This study introduces an energy-efficient strategy based on the fat tree. Here, Taylor-based Manta-Ray Foraging Optimization (Taylor-MRFO) is developed by combining the Taylor series with Manta Ray Foraging Optimization (MRFO) to distribute the load in a CDC. In load distribution, the cloud data switching to the preferred mode is done by the Actor critic neural network (ACNN). Furthermore, the developed Taylor-MRFO+ACNN provided a better outcome than the conventional approaches with the least energy consumption of 0.4930, least load of 0.3631, and least fitness of 0.4343. For setup-1, when the population size is 15, the load value obtained by the proposed method is 23.43 %, 10.19 %, 7.18 %, 5.31 %, 4.43 %, and 2.58 % higher when compared to the existing approaches namely, Artificial Bee colony(ABC), Efficient Load Optimization and Resource Minimization (ELORM), Adaptive Parameter- Ant Colony Optimization (AP-ACO), Multi-Objective Memetic Algorithm-Adaptive Plant Intelligent Behavior Optimization (MOMA-APIBO), Cooling Control Algorithm (CCA), and Minimum Total Power (MinPR). • Performance optimization and energy saving in CDC using fat tree model. • The distribution of load in CDCs is performed using Taylor-based Manta Ray Foraging Optimization (Taylor-MRFO). • Taylor-MRFO is an integration of Manta Ray Foraging Optimization (MRFO) and Taylor series. • The switching of CDC to the desired mode is performed using Actor critic neural network (ACNN). • Thus, the dual strategy leads to performance optimization in cloud infrastructure. [ABSTRACT FROM AUTHOR]

This paper benchmarks the performance of three practical electric vehicle (EV) charging scheduling methods relative to uncontrolled charging (UNC) in low-voltage (LV) distribution grids. The charging methods compared are the voltage droop method (VDM), price-signal-based method (PSM) and average rate method (ARM). Trade-offs associated with the grid performance, charging demand fulfilment and economic benefits are explored for three different grid types and four increasing levels of EV penetration for summer and winter. This study was carried out using grid simulations of six existing Dutch distribution grids, and the EV charging demand was generated based on 1.5 M EV charging sessions; therefore, the findings of this research are relevant for actual case studies. The results suggest that the PSM can be a preferred strategy for achieving a charging cost reduction of 6–11% when the grid performance is not a bottleneck for the given EV penetration. However, it can lead to an increased peak loading of the grid under certain operational conditions, resulting in a charging energy deficiency ratio of 4–8%. The VDM should be preferred if user information on the parking time and energy demand is not consistently available, and if the mitigation of grid congestion is critical. However, both unfinished charging events and charging costs increase with the VDM. The ARM provides the best balance in the trade-offs associated with the mitigation of grid congestion and price reduction, as well as charging completion. This research provides a perception of how to select the most appropriate practical charging strategy based on the given system requirements. The outcome of this study can also serve as a benchmark for advanced smart charging algorithm evaluation in the future. [ABSTRACT FROM AUTHOR]

In recent years, the increasing winter load peak has brought great pressure on the operation of power grids. The demand response on the load side helps to alleviate the expansion of the power grid and promote the consumption of renewable energy. However, the response of large-scale electric heat loads to the same electricity price curve will lead to new load peaks and regulation failure. This paper proposes a grouping coordinated preheating framework based on a demand response model, which realizes the interaction of information between the central controller and each regulation group. The room thermal parameter model and the performance map of the inverter air conditioner/heat pump are integrated into the demand response model. In this framework, the coordination mechanism is adopted to avoid regulation failure, an edge computing structure is applied to consider the users' preferences and plans, the grouping and parallel computing structure is proposed to improve the computing efficiency. Users optimize their heat load curves based on a demand response model, which can consider travel planning and ensure user comfort. The central controller updates the marginal cost curve based on the predicted scenario set to coordinate the regulation groups and suppress the new peaks. The simulation results show that the proposed method can promote the consumption of renewable energy through coordinated preheating and reduce the system energy consumption cost and user bills. The parallel computing structure within the regulation group also ensures the computing efficiency under large-scale loads. [ABSTRACT FROM AUTHOR]

Demand side management initiatives have gained attention recently because of the development of the smart grid and consumer-focused regulations. The demand side management programme has numerous goals. One of the main goals is to control energy demand by altering customer demand. This can be done in several ways, including financial discounts and behaviour changes brought about by providing knowledge to support the grid's stressed conditions. In this study, demand side management techniques for future smart grids are presented, including load shifting and strategic conservation. There are many controlled devices on the grid. The load shifting and day before strategic conservation approaches mentioned in this study are derived analytically for the minimization problem. For resolving this minimization issue, the RUNge Kutta optimizer (RUN) was developed. On a test smart grid with two service zones, one with residential consumers and the other with commercial consumers, simulations are performed. By contrasting the outcomes with the slime mould algorithm (SMA), Sine Cosine Algorithm (SCA), moth–flame optimization (MFO), and whale optimization algorithm (WOA), RUN demonstrates its effectiveness. The simulation findings demonstrate that the suggested demand side management solutions produce significant cost savings while lowering the smart grid's peak load demand. [ABSTRACT FROM AUTHOR]

Energy consumption by sector in Malaysia is rising significantly, especially for residential and commercial sectors, and is expected to continue to increase in the upcoming years. The existing power generated from a protonexchange membrane fuel cell (PEMFC) system may be insufficient to sustain peak load demands during peak periods in stationary residential applications. The presence of an ultra-capacitor (UC) bank would be beneficial as a support as it can supply a large burst of power. The integration of PEMFC and UC has the potential to provide an effective way to supply power demands, has better energy efficiency, and is also economically friendly. In this research, we demonstrate a proposed combined PEMFC and UC bank that operates in parallel. A novel design methodology and dynamic model for both PEMFC and UC systems as energy sources have been developed for stand-alone residential applications. The simulation results are shown in Matlab Simulink. These results are based on mathematical and dynamic models of the system being shown. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study investigates three different fuels such as gasoline, liquefied petroleum gas (LPG), and syngas in spark‐ignition Honda GX270 Genset engine under wide‐open throttle position on its performance, combustion characteristic as well as availability analysis. The results showed that when the engine operated with gasoline fuel, the brake thermal efficiency was higher than that of LPG and syngas by 6.2% and 7.4%, respectively, throughout the engine load condition. Brake‐specific fuel consumption of the engine with syngas (660 g/kW h) and LPG fuel (812 g/kW h) was higher than that of the gasoline fuel (510 g/kW h) at the 4.5 kW of engine load. The engine emission results showed syngas operation caused a significant reduction in NOx by 58.4%, CO by 16.5%, HC by 23.2% compared to gasoline fuel at peak load conditions. On the other hand, exergy analysis concludes the exergy efficiency for all the test fuels increases with an increase in engine load due to a high rise in shaft output. At a 4.5 kW power output, the exergy efficiency of the engine was improved to 46.45% from 45.62% and 29.73% with syngas, gasoline, and LPG, respectively. The maximum exhaust gas availability has been observed as 24.51% of availability input for syngas at 100% load condition. [ABSTRACT FROM AUTHOR]

Copyright of Unternehmung: Swiss Journal of Business Research & Practice is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An efficient vehicle thermal management is essential to fulfil the requirements of fuel consumption and passenger comfort. Therefore, the design and dimensioning of the cooling system is under high scrutiny in new vehicle architectures. With increasing electrification, no longer just the load peaks define the design frame but also the dynamics of thermal loading and recovery. Consequently, electrified vehicle architectures such as plug-in hybrid fuel cell vehicles demand for alternative approaches regarding the design of cooling systems and the definition of the decisive criteria. This article presents a new methodology for designing the cooling system related to its demands in customer operation. The recorded fleet data is first filtered for high load driving, using the so-called thermal load integral (LI) as a filter criterion. Application of the Markov Approach generates representative driving cycles out of the chosen driving sections, reducing the amount of data many times over again. Compared to other Markov models, this method also incorporates the road gradient, which is an essential information for thermal load cases. This enables the derivation of thermal load cases from fleet data. Combined with the time-weighted analysis method, a new methodology for customer-oriented cooling systems design is established. [ABSTRACT FROM AUTHOR]

With increasing the energy demand, the optimal and safe operation of power systems is the main challenge for engineers. Thus, a technique for the optimal implementation of demand response programs (DRPs), installation of distributed generation (DG) with power transmission distribution factors, and DC dynamic load flow is presented in this paper. In fact, finding the optimal time execution of DRPs and the bus for installing wind units with its probabilistic effects is considered. In this model, the congestion is decreased and the available transfer capability (ATC) rates are significantly improved. According to various types of price-based DRPs, the customers motivate to change their utilization models by shifting the price of electricity at different times. Finally, the proposed model is evaluated on the well-known IEEE 39-bus New England power system. The numerical results show the efficiency of the proposed method because, after its application, the available transmissibility values in the critical buses have significantly increased. At the same time, system peak loads, total system costs, and losses are reduced, and the voltage profile also shows a significant improvement. Totally, numerical results demonstrate that using the recommended algorithm, system loss and cost decrease by 9 percent and $4472. [ABSTRACT FROM AUTHOR]

Air-conditioners (ACs) can fully utilize the inherent characteristics of storing heat/cold in demand response (DR), achieving peak load shifting and renewable energy consumption. However, traditional control strategies based on compressor ON/OFF state require retrofitting of the internal components of ACs, making it challenging to implement directly through ACs' infrared control protocol. Given the widespread availability of infrared temperature-set-point (TSP) adjustment functions in existing ACs, this paper proposes a day-ahead optimal scheduling strategy of aggregate ACs under TSP control. Based on the second-order equivalent thermodynamic parameter (ETP) model, the TSP adjustment characteristics are considered to establish the equivalent energy storage (EES) model of ACs. On this basis, taking the overall optimization objectives of power system into account, the optimal scheduling model of aggregate ACs under TSP control is established to solve collaborative optimization problems for power generation and consumption. Case study demonstrates that, compared to optimal scheduling models based on traditional EES model, the proposed model exhibits superior peak load shifting effects and allows for smooth control of ACs through the issuance of control commands. • An optimal scheduling model of aggregate air-conditioners based on equivalent energy storage model is established. • Discrete temperature-set-point control realized through air-conditioners' infrared control protocol. • The aggregate air-conditioners' energy storage capacity and their potential in demand response are enhanced. [ABSTRACT FROM AUTHOR]

Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters. • Holistic framework for evaluating city-scale potential of green roofs. • Prototype buildings to capture various building characteristics at city scale. • GIS-informed scaling up of building-scale energy simulation toward city-scale. • Ophiopogon japonicus performing the best on city-scale green roofs in Xiamen. • Up to 1.83% energy saving and 1.63% peak load shaving by green roofs in Xiamen. [ABSTRACT FROM AUTHOR]

• The UK power grid is inadequate for 100 % EV adoption. • December's 25 % EV and HP demand exceed the 500-kW peak grid capacity. • Solar energy peaks misalign with residential community demands. • The system achieves full penetration in all seasons except winter challenges. Electric vehicles (EVs) and heat pumps (HPs) are key in reducing carbon emissions from transportation and domestic heating, yet their adoption may increase peak load demands on electrical networks. One of the aims of this research is to assess the potential impact of uncontrolled EV charging on community-scale distribution networks, exploring how this could stress the existing electrical infrastructure. It also explores the role of EVs in Vehicle-to-Grid (V2G) and smart charging applications, aiming to enhance community distribution systems. The study investigates the maximum stabilisation level achievable under various scenarios, highlighting the importance of smart energy management in integrating renewable energy and addressing uncertainties in the modelling process. Additionally, this study discusses the proposed systems' scalability, consumer behaviours' impact on the suggested energy solutions, and the potential implications of recent technological advancements for simulated communities. The research employs a sophisticated, integrative approach, combining stochastic methods with several robust energy software. Key findings suggest that uncontrolled EV charging can lead to grid capacity issues at high EV penetration levels, particularly during colder months. While smart charging and V2G technologies can moderate peak loads in many scenarios, achieving 100 % sustainable technology integration requires enhanced energy management or increased network capacity, especially in winter. Wind and solar power integration demonstrates strategic complementarity, particularly in winter, enhancing the reliability and stability of the community grid. It is also observed that peak solar generation hours misalign with the community's highest demand times, posing challenges for solar energy utilisation in EV charging in residential-based areas. [ABSTRACT FROM AUTHOR]

This research proposes and applies emission duration curves (EDCs) and emission event duration curves (EEDCs) in a novel way for comprehensive analysis of greenhouse gas (GHG) emissions in correlation with building energy consumption. Examining a 2018 high-rise building in Ottawa, we found a moderate Pearson correlation of 0.3 between hourly energy and GHG emissions, despite a significant p-value. This indicates that peak energy loads and emissions peaks do not necessarily align, underscoring the need for a new operational strategy. The EDC and Load Duration Curve (LDC) patterns further accentuated these differences, suggesting that electrical emissions are more concentrated in specific periods compared to electrical loads. For our case study, 12.04% of yearly GHG emissions occurred within just 1% of the year, contrasting with only 2.14% of the annual energy use. The study also highlighted the potential of a demand response event through a simulated voluntary outage during peak emission intervals as a viable GHG mitigation strategy. Collectively, these insights stress the importance of a comprehensive perspective on building operations, their interactions with the grid, and the necessity for tailored sustainable building management strategies moving forward. [ABSTRACT FROM AUTHOR]