Energy Supply Side Research Articles

Coordinated scheduling optimization of building integrated energy system with flexible load

Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side.

The impact of energy supply side on the diffusion of low-carbon transformation on energy demand side under low-carbon policies in China

In order to promote the synergistic green and low-carbon development of energy supply-demand sides, this paper uses the complex network evolutionary game to explore the impact of supply side decision-making on the diffusion of low-carbon transformation on demand-side under low-carbon policies. The result shows that: (1) It is feasible for the government to use carbon trading as a policy tool to promote synergistic low-carbon transformation on supply-demand sides, but it is constrained by the carbon price trend. (2) Relying only on the direct supply of clean energy from the energy supply to demand side is not enough to promote synergistic development, and the government needs to guide the both sides to cooperate in the construction of renewable energy power plants. (3) When carbon price is on an upward trend, the government needs to push forward the low-carbon transformation process on supply side, and form the low-carbon transformation of demand side driven by supply side. (4) When carbon price is on a downward trend, the government needs to guide the supply side to bear part of the transformation cost pressure on the demand side, and at the same time give more transformation subsidies.

Job losses or gains? The impact of supply-side energy transition on employment in China

The supply-side energy transition has created numerous jobs in the renewable energy industry while also destroying jobs in the traditional industry. This study examines the relationship between the supply-side energy transition and employment using panel data from China covering 2008 to 2022. It includes a heterogeneity analysis based on geographical location, economic development, and resource endowments, and estimates net employment based on clean energy production projections. Results show that a 1 % growth in clean energy production led to an approximately 0.013 % increase in total employment. However, that effect was heterogeneous. Employment in the primary industry decreased while the tertiary industry saw an increase. Geographically, employment rose in East and South China but fell in Middle China. Economically, lower-income areas benefited more, which could help reduce talent inequality. Regarding resource endowments, job losses were notable in coal-rich areas and indicated the need for fair transition. Conversely, wind and solar-rich regions experienced higher employment growth, showing a scale effect of clean energy development. By 2060, net employment is projected to increase by 5.89–9.96 million. Policies should be tailored to specific local circumstances in order to also promote a fair, equitable energy transition.

Decentralized energy management regime for prosumer community Microgrids using flexible tube model predictive control

Achieving reliable, cost-effective, and low-carbon energy management for community microgrids (CMs) under suitable computational complexity is challenging due to uncertainties in renewable generation and load demand. To address this challenge, this paper proposes a flexible tube model predictive control-based energy management regime (EMR) for prosumer CMs, enabling robustness against system uncertainties in the energy management strategy with fewer sacrifices in economic performance and computational efficiency. The proposed EMR adopts a multistage receding horizon optimization structure, which allows the prosumers to dynamically adjust their energy scheduling according to the updated energy price and forecast information. Further, a decentralized algorithm with a conditional communication strategy is developed, highlighting less communication burden and private information preservation. Case studies on a typical prosumer CM demonstrate the effectiveness of the proposed method in confronting high system uncertainties. Notably, the proposed method ensures that CMs operate with high reliability (having a maximum energy supply shortage probability of just 0.42 %), in a low-carbon and economical manner (the local accommodation rate of renewable energy generation with low costs reaches 98.83 %), even in scenarios where disturbances occur simultaneously on both the energy supply and demand sides.

Low-Carbon Distributed Optimal Operation of Integrated Energy System Considering Wind and Solar Uncertainty

To ensure the supply and demand balance of integrated energy systems (IES) in the electric-carbon market and solve the complicated interest relationship among participants in the system, a distributed optimal scheduling model based on the extended carbon emission flow theory of integrated energy systems was proposed. Firstly, hydrogen energy production and utilization devices are introduced into the energy supply side to establish an expanded carbon emission flow model of the hydrogen-energy coupling integrated energy system based on carbon emission flow theory. Then, the fuzzy membership function is used to characterize the uncertainty of wind and light output. The distributed optimization algorithm based on goal cascade analysis realizes the decoupling of the integrated energy service provider and the user. This achieves the independent solution of the integrated energy service provider. A numerical example is given to verify that the proposed strategy can realize the stable and optimal operation of the integrated energy system in an uncertain environment.

Improving capacity configuration and load scheduling for an integrated multi-sourced cogeneration system

The integration of renewable energy sources and fossil fuel e.g. coal in a heat and power cogeneration system has been proven to be an effective way to reduce CO2 emissions. An integrated multi-sourced cogeneration (IMC) system consisting of wind, solar photovoltaic (PV), and coal-fired combined heat and power (CHP) plant on the energy supply side, is studied in this paper. A bi-level collaborative optimization model for capacity configuration and load scheduling of the wind-PV-coal IMC system is developed. In the upper level, the capacity of wind farm and PV farm are optimized with the enumeration method, while the load scheduling under typical annual conditions is optimized with adaptive particle swarm optimization algorithm in the lower level. The results show that the optimal capacities of wind and PV are 120 MW and 280 MW for the IMC system, respectively. The annual and typical day load scheduling with optimal wind and PV capacities are analyzed. The result of the load scheduling shows that heating and power load distribution unevenly between the two CHP units contributes to reducing total standard coal consumption, which saves 34.96 t in a typical day during the heating season. This study can provide a reference for the capacity configuration and operation scheduling of the IMC system.

Operation Optimization of Regional Integrated Energy Systems with Hydrogen by Considering Demand Response and Green Certificate–Carbon Emission Trading Mechanisms

Amidst the growing imperative to address carbon emissions, aiming to improve energy utilization efficiency, optimize equipment operation flexibility, and further reduce costs and carbon emissions of regional integrated energy systems (RIESs), this paper proposes a low-carbon economic operation strategy for RIESs. Firstly, on the energy supply side, energy conversion devices are utilized to enhance multi-energy complementary capabilities. Then, an integrated demand response model is established on the demand side to smooth the load curve. Finally, consideration is given to the RIES’s participation in the green certificate–carbon trading market to reduce system carbon emissions. With the objective of minimizing the sum of system operating costs and green certificate–carbon trading costs, an integrated energy system optimization model that considers electricity, gas, heat, and cold coupling is established, and the CPLEX solver toolbox is used for model solving. The results show that the coordinated optimization of supply and demand sides of regional integrated energy systems while considering multi-energy coupling and complementarity effectively reduces carbon emissions while further enhancing the economic efficiency of system operations.

Low carbon optimization dispatching of energy intensive industrial park based on adaptive stepped demand response incentive mechanism

In order to reduce the operation cost and carbon emissions of the energy intensive industrial park (EIIP) system, a low-carbon optimal dispatching method considering the parameter adaptive demand response (DR) incentive mechanism is proposed. Firstly, the carbon capture system (CCS) and power to gas (P2G) equipment are introduced into the EIIP. Meanwhile, according to the different regulation types of loads’ DR capacity, a stepped DR incentive mechanism model including three parameters of interval length, compensation base price and price growth rate is proposed; Secondly, a bi-layer optimization model with the objective of minimizing the operation cost at the energy supply side and maximizing the energy efficiency at the load side of the EIIP is established. The DR Mechanism parameter optimization layer is constructed outside the two-layer optimization model, so as to establish a three-layer optimization model of the stepped DR mechanism considering parameter adaptation; The Kepler optimization algorithm (KOA) is used to optimize and implement the adaptive changes of mechanism parameters, further demonstrate the mechanism in the low-carbon optimal dispatching of EIIP; Finally, the simulation results based on multiple scenarios show that the proposed method improves the effectiveness of EIIP system operation economy and reduces carbon emissions.

Intelligent day-ahead optimization scheduling for multi-energy systems

Concerning energy waste and rational use, this paper studies the optimal scheduling of day-ahead energy supply and the community’s demand with a combined cooling, heating, and power (CCHP) system in summer. From the perspective of bilateral costs and renewable energy use, this paper examines the impact of energy storage systems integrated into cogeneration systems. The Gurobi solver is used to optimize the residential community’s supply and demand sides of the traditional CCHP system (T-CCHP) and the CCHP system with energy storage (CCHP-ESS) under insufficient solar power. Subsequently, two optimal arrangements for energy consumption on the user side under these systems are suggested. In the optimization model, energy storage is added to the T-CCHP system on the energy supply side. On the user side, the energy use scheme is optimized considering the user’s comfort. The innovation point of this study is that the optimization of comprehensive energy in the park involves both supply and demand. The impact of increasing energy storage is discussed on the energy supply side, and the impact of optimization of the energy use plan on costs is discussed on the user side.

Low-carbon optimal dispatch of campus integrated energy system considering flexible interactions between supply and demand

To solve the problem of coupled and coordinated dispatch of multi-energy between campus integrated energy system operator and users. A bi-level Stackelberg low-carbon economic dispatch model with horizontal and vertical integrated demand response and dynamic supply and demand multi-energy pricing (DSDMEP) is proposed. Firstly, the DSDMEP is established to effectively coordinate the interests of the upper CIESO and lower users. Secondly, the combined heat and power containing the power to gas and carbon capture system and the reward and penalty stepped carbon trading mechanism (SCTM) are introduced on the energy supply side to minimize carbon emissions. Then, the HVIDR is introduced to the users to increase the energy coupling and adjustable price characteristics of supply and demand. Finally, the results show that the proposed model compared to other scenarios has its carbon emissions reduced by 21.72%, the benefits of CIESO are increased by 27.86%, and the cost of the users is reduced by 6.9%. This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the SCTM on the operation of the campus integrated energy system (CIES), as well as a sensitivity analysis of the HVIDR and peak and valley electricity price difference.

Low-Carbon Oriented Collaborative Planning of Electricity-Hydrogen-Gas-Transportation Integrated System Considering Hydrogen Vehicles

Under the pressures of the energy crisis and climate change, hydrogen vehicles (HVs) have experienced significant growth in many countries recently. To promote the penetration of HVs, a low-carbon collaborative planning model based on carbon emission flow (CEF) and a modified user equilibrium theory is proposed for the electricity-hydrogen-gas-transportation integrated system. New carbon capture and storage equipment, steam methane reforming (SMR) equipment, and renewable energy equipment are planned in the model, which collaborates closely to provide green hydrogen. The proposed model considers the influence of different hydrogen production methods (electrolysis and SMR) on carbon emission reduction in the integrated energy network. Aiming to account for carbon emission from the consumption perspective, the emission caps are transferred from the energy supply side to the hydrogen production station side based on the CEF model. Additionally, the proposed modified user equilibrium model considering HV cruising range and hydrogen refueling station's location is innovatively utilized to solve the HV traffic flow assignment problem and evaluate the effectiveness of candidate roads in reducing congestion time. Overall, the total cost including equipment cost, energy consumption cost, and travel time cost is minimized to obtain the optimal integrated system planning results. Simulation results demonstrate that the proposed model is effective in reducing the carbon emissions and traffic time for the integrated network considering HV demands.

Development of an Integrated Solar-Biomass Energy System for Near-Zero Energy Buildings: A Technical and Environmental Study

The present study aims to satisfy the energy demands of a set of Norwegian residential structures with the least carbon dioxide and most renewable energy. Real-time data on building domestic hot water (DHW), heating, and electricity usage is used to plan and expand the renewable energy supply side. PVT panels provide DHW and electricity in the hybrid solar and biomass energy system. Heat is produced by the digester and heat pump. Also used is a twofold effect absorption refrigeration system for cooling. Rule-based regulation manages heat streams and redirects flows on the supply side. We provide the plant's size and execute the dynamic energy simulation. Electricity and biomass expenses determine building heating. The system is then tuned for operational conditions and compared to the design point. PVT may generate over 80% of annual DHW. Summertime radiation is more intense and can be turned into cooling energy, therefore 64.8% of cooling output comes from it. Digester/CC heats 66.55% of the structure, suggesting designers use biomass in winter due to increased energy costs. A parametric analysis shows that increasing PVT duration and tank size affects efficiency and emissions differently. Cost, efficiency, and emission index at TOPSIS are 9.73 $/hr, 36.8%, and 7.75 kg/MWh, according to optimization findings.

A data-driven distributionally robust chance constrained approach for optimal electricity-gas system operation

The extensive installation of gas-fired units and the substantial increase in natural gas consumption have strengthened the interdependence between power system and natural gas system. Therefore, the uncertainty of wind power brings new challenges to the safe and economic operation of the electricity–gas integrated energy system (IES). To optimize the operating costs of the energy supply side, an optimal electricity–gas energy flow (OEGEF) framework considering wind power uncertainty is established to maximize the social welfare (SW) of energy suppliers, energy storage suppliers and flexible users. Under this framework, based on the historical wind power data, a data-driven distributionally robust chance constrained (DRCC) model is proposed and the tractable reformulation form is given. In addition, a distributed manner via alternating direction method of multipliers (ADMM) is adopted to solve the power system sub-problems and the natural gas system sub-problems for protecting privacy data. Compared with the deterministic model, the uncertain model of Gaussian distribution and symmetrical distribution, the proposed model has stronger robustness. The case studies are conducted by two IESs of different scales, the results of numerical examples show that the model is effective for the optimal coordination of uncertain IES.

Impact of the digital economy on total factor energy efficiency: evidence from 268 Chinese cities.

Due to the advancement of digital technology, the digital economy has developed rapidly, profoundly changing human production and lifestyles, thereby promoting the dual digital transformation of the energy supply and demand sides and having a profound impact on energy utilization efficiency. Based on measuring the total factor energy efficiency (TFEE) of 268 cities in China from 2011 to 2019, we analyze the total and indirect effects of the digital economy on TFEE using a mediated effects model and examine the effects of urban heterogeneity from the perspectives of geographical location, city size, and resource endowment. The results show that the digital economy has a significant positive contribution to TFEE. In addition, the digital economy can promote TFEE through industrial structure upgrading, technological innovation, and environmental regulation. The test results of the subsample show that there is significant heterogeneity in the impact and mechanism of action of the digital economy on TFEE in different geographical locations, city sizes, and resource endowments. By understanding how the digital economy impacts TFEE, policymakers can formulate effective policies to simultaneously accelerate digital economy development and improve TFEE.

Zero-carbon-driven multi-energy coordinated sharing model for building cluster

The energy sharing among different intelligent buildings can improve renewable energy consumption and reduce carbon emission. However, the evaluation of the low carbon value of energy sharing is insufficient. Considering the different percentage of generated power of energy production units on the energy supply side during different periods, this study is the first to use time-of-use carbon emission evaluation factor to calculate the carbon emissions generated by buildings purchasing energy from upper-level networks. Then, an incentive-based carbon reward and punishment model is proposed based on the evaluation model. Meanwhile, operation decision models for different types of intelligent buildings are proposed. The optimal strategies for peer-to-peer energy sharing for intelligent buildings are obtained by the distributed algorithm. Results show that (1) The time-of-use carbon emission evaluation factor can facilitate the change of equipment output and energy consumption plans in buildings, and further improve the benefits of multi-energy sharing. (2) The proposed incentive-based carbon reward and punishment method shows greater advantages in reducing carbon emissions of high carbon emission buildings compared to the traditional carbon reward and punishment method. Although the benefits of some low carbon emission buildings with the proposed method may be sacrificed, CO2 emissions reduce by 7% for the entire building cluster.

Scheduling optimization of IES based on improved particle swarm algorithm

The integrated energy system (IES) has been a qualified candidate for solving the problems of energy utilization and environmental pollution. In this study, an improved IES scheduling optimization model based on an improved particle swarm algorithm is established, and the daily operating cost is taken as the objective function. Constraints on the energy supply and consumption sides are fully considered. The results show that with the energy storage conversion scheduling strategy participating in the optimization, the daily cost is significantly decreased by 10.5%. Compared with the genetic algorithm with elite strategy, the particle swarm algorithm is more suitable for the IES, with the lowest daily cost of $ 6, 193. Besides, the improved annealing particle swarm algorithm has better adaptability when facing high-dimensional multimodal function problems, and performs better on global search and local optimization.

Future of sustainable renewable-based energy systems in smart city industry: Interruptible load scheduling perspective

As the industrial and household loads continue to grow in smart cities, power supply and usage balance is getting more challenging. The increase in supply-side energy production is one method of alleviating peak demand proposed by a few investigations. As peak loads last for a short time, additional installations might be required. In addition, facilities that store energy can be used to reduce peak demand. As a consequence of the previous investigation, interruptible load regulation was not taken into account in optimizing system performance. The regulation of interruptible loads can greatly reduce system peak loads in smart city and within the concept of microgrids (MGs). The study proposes an interruptible load scheduling (ILS) scheme that considers consumer subsidy rates with the goal of reducing the peak load of the MG and its operating prices. To this end, a digital twin is developed in which the consumer interruption load time and minimal per-day load decrease restrictions have been completely met in the scheme. The ILS model is solved using a combination of the Min-Conflict Algorithm and the Gray Wolf Optimization Algorithm. Furthermore, simulations demonstrate the efficiency of the suggested algorithm and the show the substantial benefits of the scheme in reducing peak loads and operating prices in smart cities.

Summer heatwaves, wind production and electricity demand in Southern Europe: climatic conditions and impacts

Electricity demand for cooling and heating is directly related to weather and climate, primarily through ambient temperature. In Southern Europe, the maximum electricity demand for cooling in summer can be more pronounced than in winter, especially during heat wave (HW) episodes. With the growth of renewable technologies in the energy mix, the dependency of the electricity system on the weather is becoming evident not just from the demand side, but also from the energy supply side. From the resources point of view, summer wind presents a minimum on its annual cycle, so a combination of maximum electricity demand can coincide with a minimum of wind power production. This study presents a strong multidisciplinary focus, merging climate, energy and environmental discipline, due to their relevant connections in Southern Europe where important climate change stresses are expected. The combined anomalies of electricity demand and wind production during heat wave episodes are quantified at the country level, taking into account the HW extension. The summer period (1989-2019) of ERA5 reanalysis and E-OBS-21.0e data is used for atmospheric magnitudes and the Copernicus climate change service (C3S) energy dataset for demand. In heat wave events, an increase of 3.5%–10.6% in electricity demand and a decrease up to −30.8% in wind power production is obtained, with variability depending on the country. The greater the extension of the HW, the greater the anomalies. Different weather regimes related to heatwaves also play a role on this range of values. Therefore, the impact of extreme weather events, such as heatwaves, on wind power production in conditions of high electricity demand, should be considered in the energy supply strategy and planning in order to minimize the impact of these events on an electricity system with high penetration of renewables.

Achieving near-zero carbon dioxide emissions from energy use: The case of Sri Lanka

Signatories to the Paris Agreement are to achieve net zero Green House Gas (GHG) emissions during the half-century to pursue the efforts limiting global average temperature increase by 2 °C compared to pre-industrial levels. This study models ambitious to challenging scenarios involving energy demand and supply side actions for energy system transition toward net-zero for Sri Lanka. To analyze these scenarios a least cost optimization-based bottom-up type energy system model was developed from 2015 to 2050. A Business-as-usual (BAU) scenario and four countermeasure (CM) scenarios termed Plausible, Ambitious, Challenging, and Stringent were developed. Four different carbon tax rates were used to fathom the level of carbon tax needed to achieve net-zero emissions. The CM scenarios were formulated considering different technology options and policy measures such as the diffusion of efficient technologies, availability of renewable energy sources, use of cleaner fuels, the introduction of nuclear and carbon capture and storage technologies, and green hydrogen for power generation. The result of this study reveals that the stringent scenario which includes aggressive policy measures in both the energy supply and demand sectors, such as nuclear, and renewable energy for power generation, diffusion of efficient Enduse devices, fuel switching, including the introduction of electric cars, and increased share for public transport achieves the near carbon-neutral scenario at a carbon tax trajectory of 32 US$/tCO2 in 2020 and 562US$/tCO2 in 2050. The Net Energy Import Dependency (NEID) of the country decreases to 13 % in 2050 compared to that of the BAU scenario (65 %) under the near carbon neutral scenario, which is a positive sign from the energy security perspective.

Industry’s role in Japan’s energy transition: soft-linking GCAM and National IO table with extended electricity supply sectors

Japan’s energy transition towards carbon neutrality by 2050 will require a shift from fossil fuel energy on the energy supply side. The introduction of new power generation capacities and infrastructures will then lead to increasing demand for materials and industrial products. To capture such industrial energy service demand, we conducted a soft-linking between an integrated assessment model (GCAM) with an input–output framework (IONGES), considering both inter-model and inter-period iteration. The results show that: i) the industrial final energy under the carbon neutrality scenario would be 0.2-0.7EJ more after linking, which is almost the gap between the carbon neutrality and the reference scenario; ii) to achieve carbon neutrality by 2050, more power generation capacities would be introduced in the near-term periods (2020-2030), bringing additional growth afterward. Our soft-linking approach emphasized the role of industries in the energy transition and explored how industries can benefit from an increasingly low-carbon energy supply.