Power Load Research Articles

Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

Accurate forecasting of solar power output and load demand is critical for the efficient operation and management of isolated microgrids, where reliability and sustainability are paramount. Traditional methods often struggle with data scarcity, limitations in capturing intricate temporal dynamics, and lack of scalability. This research introduces a novel multi-task learning (MTL) model, the Self-Aware Quantized Multi-Task ConvLSTM (SAQ-MTCLSTM), which addresses these challenges by jointly forecasting solar power and load demand while leveraging shared representations across these interdependent time series. The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. This allows for effective knowledge transfer, improved data utilization, and the capture of intricate temporal patterns. Evaluated on a real-world isolated microgrid dataset from the Micro Reseau Mafate research project, the model demonstrates significant improvements in forecasting accuracy, with MSE values of 0.0021 for solar power output and 0.0037 for load demand forecasting, compared to single-task and other advanced models. Extensive experiments highlight the impact of data scarcity, seasonality patterns, and microgrid topology on forecasting performance. Such forecasting is essential to optimize the integration and utilization of renewable resources, enhancing operational stability and reducing dependency on external energy supplies.

An universal energy-matching design and regulation method for combined cooling, heating, and power (CCHP) systems in different scenarios

In this study, the impact of the coupling matching mechanism between the systems and the users on the energy-saving characteristics of the systems was analyzed based on a universal representation and computation method to identify the energy saving potential of the systems. The physical model was detailed derived for the relationship between the load supply and user demands under different capacity design modes and operation strategies to construct a universal coupling matching diagram, and the impact of the coupling matching parameters (operational energy efficiency, load matching degree, etc) on system integration and operation in different energy use scenarios was quantitative analyzed to illustrate the inherent mechanism of energy saving characteristics. In addition, the coupling mechanism between the systems and the users from a global perspective was reconstructed, and a coupling method of capacity design factor and dual source regulation was proposed to realize the collaborative integration of centralized energy supply systems, combined cooling, heating, and power systems and load demands, as well as the active regulation of the internal cascade utilization of the systems, so as to improve the energy saving potential of the systems under all operating conditions. Finally, a case analysis and method verification were conducted using typical summer days at Kunming hotel as an example. The results show that the coupling matching relationship is the internal mechanism that affects the energy saving of the systems, and the average energy saving rate of the whole day can be increased by 7.45% through collaborative integration and active regulation.

Training for Elite Team-Pursuit Track Cyclists-Part I: A Profile of General Training Characteristics.

To profile the training characteristics of an elite team pursuit cycling squad and assess variations in training intensity and load accumulation across the 36-week period prior to a world-record performance at the 2018 Commonwealth Games. Training data of 5 male track endurance cyclists (mean [SD]; age 21.9 [3.52]y; 4.4 [0.16]W·kg-1 at anaerobic threshold; 6.2 [0.28]W·kg-1 maximal oxygen uptake 68.7 [2.99]mL kg·min-1) were analyzed with weekly total training volume and heart rate, power output, and torque intensity distributions calculated with reference to their 3:49.804min:s.ms performance requirements for a 4-km team pursuit. Athletes completed 543 (37)h-1 of training across 436 (16) sessions. On-bike activities accounted for 69.9% of all training sessions, with participants cycling 11,246 (1139)km-1 in the training period of interest, whereas 12.7% of sessions involved gym/strength training. A pyramidal intensity distribution was evident with over 65% and 70% of training, respectively, performed at low-intensity zone heart rate and power output, whereas 5.3% and 7.7% of training was performed above anaerobic threshold. The athletes accumulated 4.4% of total training volume at, or above, their world-record team pursuit lead position torque (55N·m). These data provide updated and novel insight to the power and torque demands and load accumulation contributing to world-record team pursuit performance. Although the observed pyramidal intensity distribution is common in endurance sports, the lack of shift toward a polarized intensity distribution during taper and competition peaking differs from previous research.

Reliability-constrained configuration optimization for integrated power and natural gas energy systems: A stochastic approach

With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.

A Fully Analytical Approach for the Real-Time Dynamic Reliability Evaluation of Composite Power Systems with Renewable Energy Sources

Renewable energy sources (RES) have strong uncertainties, which significantly increase the risks of power imbalance and load shedding in composite power systems. It is thus necessary to evaluate the operational reliability for guiding economic dispatch and reducing the risks. Current methods cannot meet the requirement for the operational timeliness of reliability evaluations due to the high computational complexity of the optimal power flow (OPF) calculations of massive contingencies. This paper proposes a fully analytical approach to construct fast-to-run analytical functions of reliability indices and avoid reassessments when the load and RES change. The approach consists of uniform design (UD)-based contingency screening and a modified stochastic response surface method (mSRSM). The contingency screening method is used to select critical contingencies while considering the uncertainties. The mSRSM is used to construct the analytical functions of the load shedding to the load and RES generation for the selected contingencies. An analytical function of a smooth virtual variable that maps to the load shedding is established in such a way that, when the load and RES vary, the reliability can be assessed within a very short time rather than using laborious OPF calculations. Case studies illustrate the excellent performance of the proposed method for real-time reliability evaluation.

Two-stage low-carbon economic dispatch of an integrated energy system considering flexible decoupling of electricity and heat on sides of source and load

Under the goal of "double carbon", in order to further enhance the level of new energy consumption and solve the problem of restricting the flexibility of the system by "ordering power by heat" of combined heat and power (CHP) units, a low-carbon economic planning strategy with flexible decoupling of electricity and heat is proposed, by introducing a new type of electric-thermal coupling equipment on both sides of the source and load. Firstly, Consideration of the low-carbon and environmentally friendly characteristics of green ammonia production and ammonia-doped combustion technologies, a wind power(WT) – power to ammonia(P2A) - CHP units - thermal power (TH) units joint operation strategy is proposed on the source side. This strategy realizes the conversion of abandoned wind to green ammonia to ammonia coal hybrid generation, the decoupled operation of CHP units and promotes the consumption of wind power and the low-carbon operation of the system. Secondly, A dynamic incentive demand response model is developed to meet the demand of high proportion distributed PV in situ consumption on the load side. The dynamic incentive price guides the distributed power-to-heat load to change the response capacity, tracks the abandonment of wind and light, realizes the flexible conversion of power and heat load, and cooperates with the source side to promote the coupling operation of electric pyrolysis. On this basis, consider the flexible decoupling capability of electric-heat coupling equipment on both sides of the source and load to establish a two-phase low-carbon scheduling model for the day-before and day-after phases. In the day-ahead phase, the source-side electric-thermal-ammonia joint operation strategy is considered, and the electric and thermal energy supply plans are adjusted centrally; In the intra-day phase, the flexible adjustment range of power-to-heat devices and the heat load inertia on the load side are taken into account, and the electricity and heat planning strategies are adjusted in a distributed-centralised manner in conjunction with the source side. Finally, through the simulation of different cases, the results show that compared with the traditional electric heating system, the total cost of the system considering the scheduling strategy proposed in this paper decreases by ￥826,900, the carbon emission decreases by 1.2t, and basically realises the consumption of wind power and distributed photovoltaic power output. The proposed scheme reduces carbon emissions, promotes the consumption of wind power and distributed photovoltaic output, and is able to reach the goal of low-carbon economic dispatch.

Voltage stability control strategy for DC microgrid based on adaptive virtual DC motor

The large-scale integration of distributed energy sources and power electronic devices results in the DC microgrid exhibiting significant low inertia and weak damping characteristics. This, in turn, leads to inevitable fluctuations in the DC bus voltage, which endanger the stable operation of the DC system. Energy storage devices can provide equivalent inertia. To enhance the inertia and response speed of the DC bus interface converter, this paper proposes a power allocation parameter adaptive virtual DC motor control strategy based on a hybrid energy storage unit. The strategy introduces power allocation control to regulate the energy storage converter on the basis of virtual inertia parameter adaptive control, thereby enabling the energy storage converter to simulate the inertia and damping characteristics of a DC generator. The small-signal stability of the system is analyzed by establishing a small-signal model of the photovoltaic energy storage system and utilizing the impedance ratio criterion. Finally, the proposed control strategy is validated through simulation. The results demonstrate that the strategy effectively mitigates the fluctuations in bus voltage under varying photovoltaic power and sudden load changes, ensures the power distribution in the hybrid energy storage system, and enhances the dynamic response of the system.

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

Enhancing disturbance rejection in boost converter for CPL: A controller design approach with partial pole placement and approximate model matching

In DC microgrids, the cascade operation of the DC-DC converters leads to a constant power load fed by the boost converter, resulting in unstable behavior in addition to the non-minimum phase dynamics. An enhanced load disturbance rejection control scheme based on partial pole placement and an approximate model matching technique is proposed. This approach is inspired by the well-known direct synthesis method to achieve optimal load disturbance rejection performance. The effectiveness of the proposed control scheme has been validated through hardware implementation, demonstrating its capability in setpoint tracking and load disturbance rejection under variations in load power and input voltage. The robust stability of the suggested scheme has been investigated through the Kharitonov theorem and Monte-Carlo simulation, considering different performance matrices. The suitability of the presented scheme has been established by analyzing the comparative performance of recently reported works. The proposed controller reduces peak overshoot and settling time by ∼50%, handles system uncertainties up to 75%, and outperforms FOPID controllers tuned with particle swarm optimization, queen bee genetic algorithm, and chaos game optimization methods.

VMD-BiGRU for Short-term Power Load Forecasting with Energy Valley Optimizer Enhancement

Abstract Electrical load, given context of this modern era, is recognized to have distinct characteristics: diversity, flexibility, and inherently, non-linearity and temporality. Aiming to leverage these characteristics and counteract the challenges within, we propose an electrical load forecasting method, namely EVO-VMD-BiGRU. The proposed technique initiates by utilizing Variational Mode Decomposition (VMD) for the breakdown of the load data into constituent modes, extract the principal modes, and merge them with the features of air temperature, wind speed, precipitation, electricity price and holidays. The modes are then fed to the BiGRU model for training to forecast. The Energy Valley Optimizer (EVO) is applied to search for optimal hyperparameters of the BiGRU model, with the objective of minimizing the RMSE, in order to obtain models for each mode. Finally, the method utilizes these models to forecast the values of each mode, summing them to generate the final predictions. T The efficacy of this approach was confirmed through testing on load datasets sourced from Singapore and Australia. The proposed method achieved an RMSE of 81.20 MW and 30.15 MW on the respective datasets, the MAPE of 0.79% and 0.47%, and the R-squared (R2) of 0.99 and 0.99, which are superior to the alternative methods in the experiments. Results show that the proposed method achieves superior outcomes in short-term electricity load forecasting and exhibits promising potential for practical use.

Nickel-decorated ZnO nanoparticles for effective solar reduction of hexavalent chromium and removal of selected pharmaceuticals

The efficient visible light driven photocatalytic reduction of hexavalent chromium, Cr(VI) was demonstrated using ZnO nanoparticles (NPs) decorated with oxo-clusters of transition metals. The ZnO NPs were synthesized by a facile one-pot solvothermal synthesis followed by a fast microwave-assisted (MW) grafting of transition metals on the surface of NPs. Nickel was found to be the most active transition metal for photocatalytic activity as demonstrated by reduction of Cr(VI) to Cr(III). The optimally grafted samples contained 0.5 wt% Ni and increased photocatalytic activity by almost one-fold. The oxo-clusters did not enter the lattice of ZnO but rather resided on the surface and their efficient bonding to the ZnO surface was proved by Raman, TEM and X-Ray absorption techniques. Influence of MW power was studied and shown that excessive power load leads to formation of elongated structures of ZnO which decreases the photocatalytic activity. It was demonstrated by measuring fluorescent radical products that electrons, efficiently transferred via oxygen, were the main active species in combination with the unchanged oxidation power of holes and •OH in the grafted samples. The applicability of the materials was tested in immobilized plug flow photoreactor system degrading five pharmaceuticals simultaneously where their long-term use was shown.

Research on Power Grid Load Forecasting Method Based on Support Vector Machine

Abstract In order to improve the accuracy of power load forecasting, a power load forecasting model with the support vector machine (SVM) was proposed in the existing load forecasting methods. Firstly, establish the basic analysis on the SVM theoretical foundation. Through the establishment of SVM prediction model, the real power data is sent into the support vector machine prediction model for learning and forecasting once it has been processed. Think about the electricity grid load change rule states, our enterprises accurately understand the local power grid operation and scheduling measures by estimating the electricity uses. Meanwhile, it optimizes the power grid management through careful scheduling means, prevent excessive consumption of resources and large energy consumption. To achieve the prediction effect, this paper builds the prediction model and analyses the data with the MATLAB. In the experiment, the real value is compared with the anticipated value. Through the analysis of more than 8000 load data, the accuracy of the training set is 96.184%, and the accuracy of the test set is 96.6134%. The outcomes demonstrate the great accuracy of the SVM prediction model in predicting electricity load.

Large-signal stability analysis of an islanded DC microgrid with constant power loads based on mixed potential function

Abstract In DC microgrids, power electronic converters are widely used to connect loads and renewable energy sources to generate different voltages. These loads operate as constant power loads (CPL) within a closed control loop. When voltage fluctuates, these loads display negative impedance characteristics, potentially leading to system instability. Therefore, the analysis of the impact of CPL on DC microgrids during large disturbances is crucial. This paper employs mixed potential theory to study the large disturbance stability of DC microgrids under CPL. Deriving the stability criterion for the system under significant perturbations reveals the impact of load types and control parameters on system stability and provides a detailed analysis of how specific system parameters affect stability. Consequently, the stability criterion can ensure the system’s stability under large disturbances without the need for repeated calculations and simulations. Simulation results verify the correctness of this stability criterion.

A support vector regression-based interval power flow prediction method for distribution networks with DGs integration

In distribution networks with distributed generators (DGs), power generation and load demand exhibit increased randomness and volatility, and the line parameters also suffer more frequent fluctuations, which may result in significant state shifts. Existing model-driven methods face challenges in efficiently solving uncertain power flow, especially as the size of the system increases, making it difficult to meet the demand for rapid power flow analysis. To address these issues, this paper proposes an SVR-based interval power flow (IPF) prediction method for distribution networks with DGs integration. The method utilizes intervals to describe system uncertainty and employs Support Vector Regression (SVR) for model training. The input feature vector consists of the intervals of active power generation, load demand, and line parameters, while the output feature vector represents the intervals of voltage or line transmission power. Ultimately, the SVR-based IPF prediction model is established, capturing the linear mapping relationship between input data and output IPF variables. Simulation results demonstrate that the proposed method exhibits high prediction accuracy, strong adaptability, and optimal computation efficiency, meeting the requirements for rapid and real-time power flow analysis while considering the uncertainty in distribution networks with DGs integration.

Short-term power load forecasting in China: A Bi-SATCN neural network model based on VMD-SE.

This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China's "dual-carbon" policy. The integration of renewable energy under this policy has introduced complexities such as nonlinearity and instability. To enhance forecasting accuracy, the VMD-SE-BiSATCN prediction model is proposed. This model improves computational efficiency and reduces prediction errors by analyzing and reconstructing sequence component complexity using sample entropy (SE) following variational mode decomposition (VMD). Additionally, a self-attention mechanism is integrated into the temporal convolutional network (TCN) to overcome the traditional TCN's limitations in capturing long-term dependencies. The model was evaluated using data from the China Ninth Electrical Attribute Modeling Competition and validated with real-world data from a specific county in Shijiazhuang City, Hebei Province, China. Results indicate that the VMD-SE-BiSATCN model outperforms other models, achieving a mean absolute error (MAE) of 92.87, a root mean square error (RMSE) of 126.906, and a mean absolute percentage error (MAPE) of 0.81%.

Smelling-based hunting optimization for battery–Super/Ultracapacitor hybrid energy storage station in wind/solar generation system using Siamese neural network

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics.

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

The Operation Strategy of a Multi-Microgrid Considering the Interaction of Different Subjects’ Interests

As the share of renewable energy generation continues to increase, the new-type power system exhibits the characteristics of coordinated operation between the main grid, distribution networks, and microgrids. The microgrid is primarily concerned with achieving self-balancing between power sources, the network, loads, and storage. In decentralized multi-microgrid (MMG) access scenarios, the aggregation of distributed energy within a region enables the unified optimization of scheduling, which improves regional energy self-sufficiency while mitigating the impact and risks of distributed energy on grid operations. However, the cooperative operation of MMGs involves interactions among various stakeholders, and the absence of a reasonable operational mechanism can result in low energy utilization, uneven resource allocation, and other issues. Thus, designing an effective MMG operation strategy that balances the interests of all stakeholders has become a key area of focus in the industry. This paper examines the definition and structure of MMGs, analyzes their current operational challenges, compiles existing research methods and practical experiences, explores synergistic operational mechanisms and strategies for MMGs under different transaction models, and puts forward prospects for future research directions.

Multi-area short-term load forecasting based on spatiotemporal graph neural network

Short term power load forecasting can accurately evaluate the overall power load changes and provide accurate reference for power system operation decision-making. To address the limitations of traditional load forecasting methods, which are unable to capture spatial correlations and simultaneously predict load changes in multiple areas, this paper proposes a load forecasting model based on the spatiotemporal attention convolutional mechanism. The proposed model is composed of two key components: a spatiotemporal attention module and a spatiotemporal convolution module. First, the dynamic spatiotemporal correlations between different electricity load areas are captured and analyzed by using the spatiotemporal attention mechanism. Secondly, the spatial pattern and temporal features of the load data sequence are effectively obtained by spatiotemporal convolutional layers. Finally, this paper verifies the accuracy and effectiveness of the proposed method through a real electricity consumption load dataset. The experimental results demonstrate that, in comparison to various benchmark prediction methods, the proposed method can fully explore and utilize the spatiotemporal correlation between different electricity load areas, thereby improving the accuracy of load prediction.

Low carbon operation and evaluation methods for integrated energy system counting EVs with whole life cycle considering demand response

The integrated energy system has gradually become an effective way of energy saving and emission reduction with the proposal of “dual carbon" target and the continuous promotion of low-carbon development. However, most of the existing studies on demand-side flexible loads only consider flexible power loads, and there are fewer studies on the controllability of thermal loads. This paper proposes a low-carbon and economic optimal scheduling model and evaluation method for integrated energy system considering demand-side response. With the goal of minimizing the total operating cost of the integrated energy system, and taking into account the transferable and curtailing characteristics of the electric and thermal flexible loads, we establish an optimal scheduling model of the integrated energy system considering the user-side flexible loads, and comprehensively evaluate the performance of the system in terms of economic performance, reliability, and environmental performance. The performance of the system is evaluated in terms of economic performance, reliability and environmental performance. The role of flexible loads in improving the economy of the combined supply system is investigated with examples, and the rationality and effectiveness of the study are verified through comparative analysis of different scenarios. The results show that the total cost of the system is reduced by 18.04 %, 9.1 %, 3.35 % and 7.03 % when carbon trading cost and demand-side flexible electric-heat load response are considered at the same time; and the total carbon emission of the system is reduced by 65.28 %, 20.63 %, 3.85 % and 18.03 %, respectively. Considering carbon trading costs and demand-side flexible electric and thermal load response can improve the reliability of the system. The overall reliability of the system is improved by 10 %, 15 %, 16.67 % and 20 % for the four scenarios, respectively.