Supply-demand Balance Research Articles

Innovative heat management method and metaheuristic algorithm optimized power supply-demand balance for PEMFC-ASHP-CHP system

The evolution of distributed building energy systems fuels the growing demand for sustainable energy solutions. In this paper, Proton Exchange Membrane Fuel Cell (PEMFC) and Air Source Heat Pump (ASHP) were integrated to form PEMFC-ASHP-CHP systems in three combination methods, i.e., Direct Combination (DC), Parallel Combination (PC), Series Combination (SC). And compared the energy management strategies and power balance of the system. To further improve reliability and flexibility, a diversion PEMFC-ASHP-CHP system was proposed by combining PC and SC's system advantages. Additionally, an iterative algorithm addressed the mismatch between power supply and demand. An empirical formula was proposed to improve iterative convergence speed for practical control situations. The coefficients were optimized using six metaheuristic algorithms, and the outcomes were summarized into an optimized operational plane to enhance the system control response speed further. The results show that the PC method performs better than DC and SC. It achieves a power consumption reduction of 52.8% and a COP improvement of 111.4% compared with the ASHP system. Meanwhile, the diversion system can more effectively utilize waste heat from the PEMFC and further improve the system's performance. The Queuing Search Algorithm (QSA) demonstrates superior accuracy for coefficient optimization. By using the established empirical formula, the convergence speeds of global and local iterative methods are improved by 26.10% and 41.78%, respectively, compared to the direct iterative algorithm. Ultimately, the optimized operational plane can achieve a maximum 37.16% hydrogen consumption reduction compared to the unoptimized system.

Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load

The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.

Management of a hybrid renewable energy storage system

The development of smart grids, made possible by advancements in computer technology, represents a transformative solution for electricity grids. These systems offer significant improvements in terms of security, reliability, and energy efficiency, while also promoting a low-carbon economy. Smart grids enable more efficient management of electricity supply and demand, as well as optimization of energy storage. However, despite these advantages, challenges persist, particularly in the areas of cybersecurity, and the reliability and speed of information processing. To address these challenges, we propose an innovative algorithm designed for energy management within electricity grids. This algorithm, specifically tailored for grids comprising hybrid storage systems, photovoltaic energy sources, and variable loads, aims primarily to balance supply and demand. This comprehensive approach not only ensures a continuous power supply but also reduces vulnerabilities associated with reliance on a single energy source. To evaluate the effectiveness of this algorithm, we conducted a series of rigorous simulation experiments using the MATLAB platform. These simulations meticulously tested the system's performance, providing substantial experimental evidence of its durability and viability in real-world scenarios. In summary, smart grids offer promising opportunities to enhance the security of electricity grids, maintain supply-demand balance, and promote sustainable energy storage practices. Despite persistent challenges in cybersecurity and information processing, our proposed algorithm represents a significant advancement in addressing these issues and strengthening the capabilities of smart grids.

Effects of technical advance on cobalt supply and its supply trends: A perspective of the whole industrial chains

By coupling dynamic material flow analysis and econometric models, the effects of technical advance on cobalt supply in the whole industrial chains are explored. Then, its supply trends are investigated at the global and national levels. The results show that: (1) the impact of technical advance on cobalt supply in the mining stage is inverted U-shape, while in the other stages, they are linear promotions. (2) Technical advance could increase global primary supply by up to 10 % from current production capacity. By 2030, secondary and total supplies will reach 79 kt and 251 kt or even more. (3) Refined cobalt supply share of China is expected to be 76 % by 2030. The European Union will produce the largest secondary supply, followed by China, the United States and Japan. (4) With the simultaneous development of technologies in each stage, supply-demand balance could be realized under some ideal scenarios. The above findings help intuitively recognize the roles of technical advance and strengthen the determination to do it.

Enhancing Resource Allocation for Multi-Energy Storage Systems: A Comprehensive Approach Considering Supply and Demand Flexibility and Integration of New Energy

This study presents an innovative optimization method for resource scheduling in multi-energy storage systems, focusing on improving resource allocation while considering supply-demand flexibility and renewable energy integration. As renewable energy gains popularity and multi-energy systems become more complex, effective utilization of energy storage to achieve supply-demand balance, optimize energy scheduling, and maximize renewable energy integration is crucial. To address this challenge, a Markov dynamic model is developed to capture the dynamic changes in energy supply and demand within the multi-energy storage system. The model is then solved using a reinforcement learning approach to optimize resource scheduling decisions. Numerical simulations and case studies are conducted to validate the effectiveness and feasibility of the proposed method, showcasing its potential to enhance operational efficiency and reliability in multi-energy storage systems amidst constantly changing energy patterns. This research provides valuable insights and decision support for the design and operation of multi-energy storage systems, contributing to the advancement of sustainable energy utilization and promoting sustainable development in the energy sector.

A stochastic programming model for free-floating shared bike redistribution considering bike gathering

We study a stochastic free-floating shared bike redistribution problem, which first gathers bikes randomly scattered around the hotspots of a city and then transships the gathered bikes among the hotspots to achieve a better supply–demand balance. In the literature, the bike gathering operation and the associated stochasticity, although significant for shared bike redistribution practice, are rarely considered. To fill the gap, we propose a two-stage stochastic programming model that explicitly incorporates the bike-gathering cost with two formulations. In the planning stage, the deployment of gathering sites and the allocation of gathering staff are determined. In the operation stage, the gathering, transshipment and replenishment of shared bikes are optimized based on the decisions of the planning stage and the realization of the uncertain supply and demand of free-floating shared bikes in space. With a case study based on real data from the Mobike company in Beijing, China, we illustrate the real-world applications of our proposed model, highlight the importance of considering the cost of bike gathering when planning free-floating shared bike redistribution, and gain managerial insights for better field practices.

Exploring an assessment framework for the supply–demand balance of carbon sequestration services under land use change: Towards carbon strategy

The key challenge created by global climate change is to implement effective carbon–neutral programs. The carbon sequestration service (CSS) establishes spatial and temporal connections between carbon sequestration supply and demand, so accurate scientific quantification of the CSS supply–demand balance is essential. However, a standard assessment framework for quantifying CSS supply–demand is currently lacking. In this study, we developed a spatio-temporal assessment framework for quantifying CSS supply–demand balance under land use change, based on the SPANs model. We followed the flow of CSS from supply to demand areas through spatial visualization based on the main wind direction and defined the transport path and quantity of CSS flow, taking Lancang Mekong River Basin (LMRB) as a case. The results showed that: (1) The total supply of CSS decreased by 1.14 million tons from 2000 to 2020, and total demand for CSS increased by 8.1 million tons. (2) CSS surplus areas were mainly concentrated in forested regions, with a maximum surplus of 4.31 t/ha. Deficit areas were dominated by cultivated land and artificial surfaces, with a maximum deficit of −1101.21 t/ha. There was spatial heterogeneity in CSS distribution and a clear imbalance between supply and demand. (3) Three out of six countries in the basin showed significant changes in CSS flow, with a decrease in demand area observed in Thailand and an increase in demand area observed in Cambodia and Vietnam. (4) CSS flow was mainly from north to south, with higher CSS in Thailand and lower flow in China. Thus the analysis revealed supply–demand imbalances in CSS and provided an objective understanding of the spatial flow of CSS. This scientific and intuitive theoretical basis and data support can be used for regional carbon management in LMRB and global carbon balance control.

Changes in oxygen supply–demand balance during induction of general anesthesia: an exploratory study using remimazolam

PurposeThis study was performed to evaluate the changes in oxygen supply–demand balance during induction of general anesthesia using an indirect calorimeter capable of measuring oxygen consumption (VO2) and carbon dioxide production (VCO2).MethodsThis study included patients scheduled for surgery in whom remimazolam was administered as a general anesthetic. VO2 and VCO2 were measured at different intervals: upon awakening (T1), 15 min after tracheal intubation (T2), and 1 h after T2 (T3). Oxygen delivery (DO2) was calculated simultaneously with these measurements. VO2 was ascertained using an indirect calorimeter and further calculated using vital signs, among other factors. DO2 was derived from cardiac output and arterial blood gas analysis performed with an arterial pressure-based cardiac output measurement system.ResultsVO2, VCO2, and DO2 decreased significantly from T1 to T2 and T3 [VO2/body surface area (BSA) (ml/min/m2): T1, 130 (122–146); T2, 107 (83–139); T3, 97 (93–121); p = 0.011], [VCO2/BSA (ml/min/m2): T1, 115 (105–129); T2, 90 (71–107); T3, 81 (69–101); p = 0.011], [DO2/BSA (ml/min/m2): T1, 467 (395–582); T2, 347 (286–392); T3, 382 (238–414); p = 0.0020]. Among the study subjects, a subset exhibited minimal reduction in VCO2. Although the respiratory frequency was titrated on the basis of end-tidal CO2 levels, there was no significant difference between the groups.ConclusionGeneral anesthetic induction with remimazolam decreased VO2, VCO2, and DO2.

Demonstration of denial of charging attack on electric vehicle charging infrastructure and its consequences

The recent upsurge in electric vehicle (EV) adoption has led to greener mobility but has also broadened the attack surface due to the increased interconnection between the entities like EV, EV charger, grid etc. We show in this paper that among these entities, the EV charger provides a possible attack surface through the available communication network. Adversaries at a minimum can disrupt the vehicular charging process known as denial of charging (DoC) attack. This attack is demonstrated on the real hardware setup of an EV charging, where we have considered the Bharat EV DC charging standard (BEVC-DC001) adopted by India which uses the controller area network (CAN) bus to communicate between EV charger and EV. The DoC attack can have significant consequences both on the electrical grid as well as individuals. The EV chargers (with connected EV) collectively serve as a large load demand, whose sudden inaccessibility would disrupt the supply–demand balance, triggering over frequency relays to either cause local or national blackout. Such a scenario is presented in this work on a microgrid (MG), in a real-time OPAL-RT environment. Not only can this attack lead to major transportation related problems but would also disrupt medical and emergency services.

Energy scheduling method for island micro-grid system based on prediction model

Abstract Renewable energy, due to its characteristics such as volatility and intermittency, brings difficulties to the energy scheduling of renewable energy power generation systems. The main way of island power generation is to use renewable energy power generation. This chapter proposes a hierarchical energy scheduling method based on the prediction model through the research on the energy scheduling method of the island microgrid system to realize the energy optimization between the microgrid sub-systems, to achieve the maximum consumption of renewable energy for power supply, and to reduce the power supply cost and improve user satisfaction. The effectiveness of this method in improving the energy supply-demand balance of the microgrid sub-systems is verified through experiments.

Grid dispatching model and benefit analysis of concentrating solar power plants considering flexibility

Abstract With the increasing penetration rate of renewable energy with volatility and uncertainty, flexibility has become an important factor to consider in the operation of the power system. To study the grid-connected dispatching and benefit analysis of CSP plants considering flexible supply-demand balance in the electricity market, the output level of CSP is modeled according to the internal steady state energy flow relationship. Secondly, we model the supply-demand characteristics of flexible resources. Finally, we establish an optimization dispatching model that considers the penalty of insufficient flexibility. The calculation example verifies the effectiveness of considering flexibility supply-demand balance and CSP plants in improving system flexibility.

Stochastic and Extreme Scenario Generation of Wind Power and Supply–Demand Balance Analysis Considering Wind Power–Temperature Correlation

In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results.

Exploring the Response of Ecosystem Services to Socioecological Factors in the Yangtze River Economic Belt, China

Understanding the response of the mechanisms driving ecosystem services (ESs) to socioecological factors is imperative for regional sustainable ecosystem management. However, previous studies of the mechanisms driving ESs have focused more on the degree and direction (positive and negative) of effects on ES supply or the supply–demand balance, while their nonlinear response processes have not been fully considered. In this study, a theoretical framework was developed through integrating land use/land cover data and supply and demand matrices with random forest models to assess response processes, including the relative importance and marginal effects, of essential factors that drive ES demand, supply, and supply–demand balance. Using the Yangtze River Economic Belt (YREB) as an example, our results indicated that the ES deficit regions (332 of 1070 counties or 14.45% of the area) of the YREB were located mainly in the three national urban agglomerations. Moreover, this study indicated that natural environmental factors (such as slope and precipitation) significantly influence the supply and supply–demand balance of ESs, while socioeconomic factors (such as cropland ratios and population density) profoundly influence the demand for ESs. However, cropland ratios were the most important drivers of ES supply, demand, and supply–demand balance in the YREB. Moreover, three types of response processes were identified in this study: logarithmic increase, logarithmic decrease, and volatility increase. Specific driving factors (e.g., proportion of cropland area, precipitation, population density, and slope) had significant threshold effects on the supply–demand balance of ESs. The turning points that can be extracted from these response processes should be recommended for ecosystem restoration projects to maintain regional sustainable ecosystem management.

A centralized EMPC scheme for PV-powered alkaline electrolyzer

Photovoltaic (PV)-powered alkaline electrolyzer system (PVPAES) is an advanced technique to convert the off-grid and intermittent PV-based solar energy into storable and transportable electrolyzer-based hydrogen energy with zero carbon emissions. However, it is difficult to realize the coordinated control of the off-grid PV module and the alkaline electrolyzer, due to the multiple timescale dynamics. To address this challenge, the PVPAES is decomposed into the slow part and fast part based on the dynamic time scale. Exploiting the decomposed subsystems, the slow one is assumed to be managed well by the auxiliary controller. For the fast one, a centralized economic model predictive control (CEMPC) scheme is constituted. This CEMPC integrates the energy management system and local feedback control into a single optimal control framework. A mathematical model of the PVPAES is established, on the basis of which the CEMPC directly adopts the economic indices as the cost function to realize the flexible power point tracking, power supply-demand balance, and dynamic economic optimization. Moreover, the inherent strong nonlinearity of PVPAES results in the nonconvex mixed-integer nonlinear programming optimization problem in the CEMPC. The exhaustive search algorithm utilizing finite converter switching states is adopted to achieve the global economic optimum. The effectiveness of the proposed CEMPC controller is illustrated through simulations under varying irradiance conditions.

Study on integrated energy microgrid energy purchase strategy with demand-side response in market environment

Integrated energy microgrids (IEM) have emerged as an effective way to improve energy efficiency and promote distributed energy utilization. IEM systems acquire electricity and gas from external markets and supply electricity/heat/cold to users. In this paper, we study the optimal energy purchase strategy for IEM, considering the impact of demand response incentives. Firstly, considering the uncertainties, we construct an IEM medium- and long-term market multi-energy purchase model based on conditional value-at-risk, optimizing the portfolio of electricity and gas purchases, as well as their proportion in total energy amount. Subsequently, based on medium- and long-term daily energy supply curves and day-ahead load forecast results, a spot market energy purchase model is established to optimize the spot purchase of electricity and gas, maintaining the supply-demand balance while minimizing operating costs. Furthermore, we design demand response incentives and develop a master-slave game model between IEM and users to guide the formulation of the energy purchase strategy by incorporating corrected load data as feedback. The energy purchase strategies are resolved by the GUROBI solver, while the optimization of demand response incentives is carried out through the PSO algorithm, all based on the MATLAB platform. The adaptability of the proposed model and strategy is verified.

Mathematical Modeling of Drinking Water Losses Management Case Study – Cairo - Egypt

Water, As A Precious natural resource, necessitates sustainable management to minimize wastage. Excessive leakage, particularly in regions confronting water scarcity such as Egypt, directly undermines environmental objectives and principles of cost recovery. Effective water management is pivotal for reducing losses and optimizing efficiency. Accurate measurement of water production and distribution is essential to identify losses and assess water utilities' performance. This data aids in pinpointing the root causes of losses, whether physical or commercial, facilitating targeted reduction strategies. Forecasting supply-demand balance is critical for planning water networks and treatment facilities. Detailed infrastructure information enables decision-makers to discern areas experiencing water shortages and those with surplus resources. Our aim is to probe the causes of water losses in Cairo's drinking water network, the capital of Egypt, and propose managerial solutions. Operational efficiency relies on minimizing labor, material, and energy costs. Water loss poses a significant operational challenge, as components of non-revenue water can significantly impact utilities' financial stability. The IWA Water Balance offers a standardized approach to water audits, facilitating a comprehensive analysis of water production, storage, and distribution. This analysis aids in quantifying the magnitude of the water loss problem and prioritizing corrective actions based on revenue and non-revenue water components. The Cairo Water Company is entrusted with producing 5.5 million cubic meters of purified drinking water per day from 11 treatment plants, serving a network spanning 13,307 kilometers and supported by 20 maintenance centers. With a focus on serving 1.1 million citizens in compliance with Egyptian regulations, our study encompasses various metrics, including billed and unbilled consumption, unauthorized usage, and metering inaccuracies, to calculate commercial losses. Additionally, we evaluate main and storage leakages, as well as service connection leakages up to the customer meter, to determine technical losses. Through a mathematical module, we compute non-revenue water (NRW) and infrastructure leakage index (ILI) metrics. Performance indicators are utilized to assess the effectiveness of water utilities in managing losses and asset maintenance. Our designed module empowers decision-makers to evaluate and enhance asset methodologies for water loss management.

Kernel Function Selection for FISVDD Applied to Outliers Detection Application for Energy Time Series Datasets

Forecasting energy consumption is crucial for maintaining stability in the energy supply-demand balance. This process requires accurate data, necessitating deep preprocessing steps, with outlier detection being a pivotal task. While most existing methods are tailored for batch learning, smart grid data collection operates continuously, demanding online algorithms for effective data cleaning. This paper proposes an analytical study emphasizing the significance of selecting an appropriate kernel function for data description methods and its impact on forecasting efficiency. The Fast Incremental Support Vector Data Description (FISVDD) algorithm is chosen as a kernel-based approach for outlier detection in time series datasets. The evaluation involves different kernel functions on two datasets: Electrical Grid Stability Simulated Data and Individual Household Electric Power Consumption Dataset. Criteria such as the objective function and data distribution are considered to determine the most suitable kernel function. FISVDD's performance with the right kernel function is compared with other outlier detection methods, and the results are fed into a Gated Recurrent Unit model to visualize the impact on forecasting. Mean Squared Error evaluation reveals that FISVDD with the appropriate kernel function yields the best results, underscoring the strength of data description methods with the appropriate kernel function.

Defining the land use area threshold and optimizing its structure to improve supply-demand balance state of ecosystem services

Defining the land use area threshold and optimizing its structure to improve supply-demand balance state of ecosystem services

Sulfonylureas Compromise Microvascular Flow in Mouse Skeletal Muscle

Sulfonylureas are among the most prescribed oral anti-hyperglycaemics, particularly in low- and middle-income countries. Sulfonylureas are insulin secretagogues through blocking ATP-sensitive potassium channels (KATP) in β-cells of the pancreas. Despite wide usage, sulfonylureas are associated with increased cardiovascular disease morbidity and all-cause mortality, with ill-defined mechanisms. We tested the hypothesis that glyburide could block KATP channels in the microvasculature, thereby disrupting tissue oxygen demand-supply balance. We further investigated the contribution of other potassium channels to capillary-to-arteriole signalling. Experiments were conducted using an in-vivo anesthetized mouse skeletal muscle preparation (extensor digitorum longus, EDL); exteriorized and situated at in-situ length, overtop an oxygen-permeable membrane covering a gas chamber, and bathed in PlasmaLyte. C57BL/6 mice were employed (n=6-8). Brightfield images were captured at two wavelengths: oxygen-dependent and oxygen-independent, enabling the simultaneous determination of capillary hemodynamics and RBC oxygen content. Low oxygen challenges were achieved by dropping oxygen levels in the gas chamber from 7% to 2% for 3 minutes. Challenges were conducted before (control) and after drugs were added to bathing solution. Parameters were obtained before the challenge (baseline), during the challenge (2 regions) and after the challenge (recovery). Statistical significance was determined using a paired Student’s t-test. Target channels localization was investigated using immunofluorescence in formalin-fixed EDL. Oxygen challenges augmented EDL surface capillary hematocrit and RBC velocity and supply rate, while reducing RBC oxygen saturation. Incubation of the EDL with 10 μM glyburide decreased the baseline RBC supply rate (p<0.05 vs control) and blunted the magnitude of capillary responses to the oxygen challenge (p<0.05 vs control). Immunofluorescence confirmed the localization of KATP channels in EDL capillaries. Normal capillary responses were obtained when small and intermediate conductance or inward rectifier potassium channels were neutralized. This implies minimal or no contribution of these channels in oxygen signaling in the microvasculature. This data begins to elucidate the acute and direct effects of systemic KATP blockers on capillary-to-arteriole conducted signaling, which is likely key to optimal tissue oxygen regulation. Further experiments are warranted to confirm this conclusion. This work was supported by the Natural Science and Engineering Research Council of Canada (NSERC, RGPIN/04659-2017), an Ontario Graduate Scholarship (M.A.E) and a CIHR doctoral award (M.A.E). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Real time aggregation control of P2H loads in a virtual power plant based on a multi-period stackelberg game

Power to Heat (P2H) technology is seen as an effective solution for integrating stochastic wind power. Particularly in real-time electricity markets, high-precision wind power forecasts enable the load to better accommodate its fluctuations. Nonetheless, the shorter time scales of real-time trading pose challenges for efficiently aggregating a large, heterogeneous, and dispersed loads and fully harnessing the thermal storage capabilities of loads to follow wind power fluctuations across multiple periods. Against this backdrop, this paper proposes an aggregation control approach for P2H loads with considering multi-period Stackelberg game strategy within the framework of a load-oriented virtual power plant (VPP). For multi-period real-time trading, a Stackelberg game model based on a dynamic price ceiling is established with considering dynamic supply-demand balance between wind power and P2H loads; To enhance load aggregation effectiveness, a dynamically updated temperature-power aggregation model is developed by employing model order reduction, variables continuousization, and linear superposition on second-order equivalent thermodynamic model; To improve load decomposition efficiency, a hierarchical and multi-step control strategy encompassing the continuousization of discrete variables is utilized to simplify the problem. Finally, a simulation example is presented using actual data from a power grid in northern China to validate the effectiveness of the proposed method.