Search Results (11)

In the context of dual-carbon goals, Park-Level Integrated Energy Systems (PIES) are pivotal for enhancing renewable energy integration and promoting clean, efficient energy use. However, the inherent non-linearity from multi-energy coupling and the high dimensionality of operational data present substantial challenges for conventional scheduling optimization methods. To overcome these obstacles, this paper introduces a novel multi-objective scheduling framework for PIES leveraging deep reinforcement learning. We innovatively formulate the scheduling task as a Markov Decision Process (MDP) and employ the Trust Region Policy Optimization (TRPO) algorithm, which is adept at handling continuous action spaces. The state and action spaces are meticulously designed according to system constraints and user demands. A comprehensive reward function is then established to concurrently pursue three objectives: minimum operating cost, minimum carbon emissions, and maximum exergy efficiency. Through comparative analyses against other AI-based algorithms, our results demonstrate that the proposed method significantly lowers operating costs and carbon footprint while enhancing overall exergy efficiency. This validates the model’s effectiveness and superiority in addressing the complex multi-objective scheduling challenges inherent in modern energy systems. Full article

Accurate multi-energy load forecasting is essential for the low-carbon, efficient, and resilient operation of park-level Integrated Energy Systems (PIESs), where cooling, heating, and electricity networks interact closely and increasingly incorporate renewable energy resources. However, forecasting in such systems remains challenging due to complex cross-energy coupling, high-dimensional feature interactions, and pronounced nonlinearities under diverse meteorological and operational conditions. To address these challenges, this study develops a novel three-stage hybrid forecasting framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV), a Multi-Task Long Short-Term Memory network (MTL-LSTM), and Random Forest (RF). In the first stage, RFECV performs adaptive and interpretable feature selection, ensuring robust model inputs and capturing meteorological drivers relevant to renewable energy dynamics. The second stage employs MTL-LSTM to jointly learn shared temporal dependencies and intrinsic coupling relationships among multiple energy loads. The final RF-based residual correction enhances local accuracy by capturing nonlinear residual patterns overlooked by deep learning. A real-world case study from an East China PIES verifies the superior predictive performance of the proposed framework, achieving mean absolute percentage errors of 4.65%, 2.79%, and 3.01% for cooling, heating, and electricity loads, respectively—substantially outperforming benchmark models. These results demonstrate that the proposed method offers a reliable, interpretable, and data-driven solution to support refined scheduling, renewable energy integration, and sustainable operational planning in modern multi-energy systems. Full article

With the deepening of multi-energy coupling and the integration of high proportions of renewable energy, the Park Integrated Energy System (PIES) 1demonstrates enhanced energy utilization flexibility. However, the random fluctuations in photovoltaic (PV) output also pose new challenges for system dispatch. Existing distributed robust scheduling approaches largely rely on offline predictive models and therefore lack dynamic correction mechanisms that incorporate real-time operational data. Moreover, the initial probability distribution of PV output is often difficult to obtain accurately, which further degrades scheduling performance. To address these limitations, this paper develops a PV digital twin model capable of providing more accurate and continuously updated initial probability distributions of PV output for distributed robust scheduling in PIESs. Building upon this foundation, this paper proposes a distributed robust scheduling method for the PIES based on digital twins. This approach aims to maximize the flexibility of energy utilization in PIESs and overcome the challenges posed by random fluctuations in PV output to PIES operational scheduling. First, a PIES model is established after investigating a park-level practical integrated energy system. To describe the uncertainty of PV output, a PV digital twin model that incorporates historical data and temporal features is developed. The long short-term memory (LSTM) neural network is employed for output prediction, and real-time data are integrated for dynamic correction. On this basis, error perturbations are introduced, and PV scenario generation and reduction are carried out using Latin hypercube sampling and k-means clustering. To achieve multi-energy cascade utilization, the objective of optimization is defined as the minimization of the sum of system operating cost and curtailment cost. To this end, a two-stage distributed robust optimization model is constructed. The optimal scheduling scheme was obtained by solving the problem using the column-and-constraint generation (CCG) algorithm. The proposed method was finally validated through a case study involving an actual industrial park. The findings indicate that the constructed digital twin model achieves a significant improvement in prediction accuracy compared to traditional models, with the root mean square error and mean absolute error reduced by 13.3% and 10.81%, respectively. Furthermore, the proposed distributed robust scheduling strategy significantly enhances the operational economics of PIESs while maintaining system robustness, compared to conventional methods, thereby demonstrating its practical application value in PIES scheduling. Full article

Renewable energy systems in the Industry 4.0 era have maintenance and production maximization as their central element, depending on the type of source. For solar panels, achieving these goals requires periodic cleaning of dust deposits. This research integrates the detection of dust particles on solar panels using classification models based on machine learning models integrated into the Azure platform. However, the main contribution of the work does not lie in the development or improvement of a classification model, but in the design and implementation of an Internet of Things (IoT) hardware–software infrastructure that integrates these models into a complete predictive maintenance workflow for photovoltaic parks. The second objective focuses on how the identification of dust particles further generates alerts through a centralized platform that meets the needs of Industry 4.0. The methodology involves analyzing how the Azure Custom Vision tool is suitable for solving such a problem, while also focusing on how the resulting system allows for integration into an industrial workflow, providing real-time alerts when excessive dust is generated on the panels. The paper fits within the theme of the Special Issue by combining digital technologies from Industry 4.0 with sustainability goals. The novelty of this work lies in the proposed architecture, which, unlike traditional IoT approaches where the decision is centralized at the level of a single application, the authors propose a distributed logic where the local processing unit (Raspberry Pi) makes the decision to trigger cleaning based on the response received from the cloud infrastructure. This decentralization is directly reflected in the reduction in operational costs, given that the process is not a rapid one that requires a high speed of reaction from the system. Full article

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. Full article

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article

The asymmetry-induced uncertainty in both sources and loads is a crucial and continuously spotlighted issue within modern power systems. Applying optimization scheduling method to deal with this asymmetry is a feasible solution. Accordingly, this paper proposes a bi-level park-level integrated energy system (PIES) optimization strategy considering uncertainties of price-based load demand response (PLDR). Firstly, a model for characterizing the uncertainties of the PLDR is developed based on fuzzy theory. Secondly, a bi-level two-stage PIES optimization model that includes multiple device models is established. In the first stage, the dynamic pricing optimization is carried out with the aim of maximizing user satisfaction. In the second stage, the PIES scheduling strategy optimization is performed with the aim of minimizing the operation costs of PIES. Finally, multiple scenarios are set to conduct comparative validation, which demonstrates that the proposed method not only improves the renewable energy integration capacity of the system, optimizes the load profiles, and enhances the economic and low-carbon performance, but also increases user satisfaction, thus providing a reference for the dispatch and operation of the park-level integrated energy system. Full article

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. Full article

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers’ energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism. Full article

Operation strategies for a park-level integrated energy system (PIES) in terms of carbon prices and feed-in tariffs, have not been adequately studied. This paper addresses this knowledge gap by proposing operation strategies based on the PIES driven by biogas, solar energy, natural gas, and the power grid. Meanwhile, the electricity-driven dispatching strategy (EDS), thermal-driven dispatching strategy (TDS), cost-driven dispatching strategy (CDS) are compared to assess their impacts on operation cost, carbon dioxide emissions, etc. The flexibility and complementarity of the three operation strategies in energy supply are analyzed in detail. The results indicated that biogas was the main energy supply fuel, accounting for 46% to 72% of the total energy supply. About 33% to 54% of electricity was transmitted to the grid each month using the TDS. The annual initial capital cost of the CDS was only 1.39% higher than that of the EDS. However, the annual operation cost of the EDS was 16.86% higher than that of the CDS. The emissions of the EDS were the lowest, and the CDS had 38.51% higher emissions than the EDS. In the CDS, the ratio of carbon emission costs to operation costs was as high as 0.80 when the carbon tax reached USD 100/ton. The carbon tax had a greater impact on the CDS than the other strategies. Feed-in tariffs had a greater impact than the carbon tax on the TDS. This study provides an effective method for the selection of optimal operation strategies in regards to carbon prices and feed-in tariffs. Full article

In the face of the dual crisis of energy shortages and global warming, the vigorous development of renewable energy represented by wind-solar energy is a significant approach towards achieving energy transition, carbon peaking, and carbon neutrality goals. Targeting the park-level integrated energy system (PIES) with high penetration of wind-solar energy, we propose a day-ahead dispatching strategy that takes into account the flexible supply and the reward-punishment ladder-type carbon trading mechanism (RPLTCTM). Firstly, RPLTCTM and carbon capture equipment (CCE) are considered in the dispatching model, and the mechanism of coordinated operation of CCE and RPLTCTM is explored to further improve the system’s ability to restrain carbon emissions. Secondly, power-based flexibility indicators (PFIs) are adopted to quantitatively evaluate the flexibility supply, and based on the load demand response characteristics, the dispatchable resources on the load side are guided to improve the system’s operation flexibility. On this basis, a multi-objective optimal dispatching model that takes into account the carbon emission cost, energy cost, and flexibility supply are constructed, and the original problem is transformed into a mixed-integer single-objective linear problem through mathematical equivalence and flexibility cost. Finally, simulation examples validate that the economy, flexibility, and low-carbon level of the dispatching plan can be synergistically improved by the proposed strategy. Full article