Integrated energy system operation optimization with gas linepack and thermal inertia

Guest Editorial: Situational awareness of integrated energy systems

To achieve the low‐carbon transformation of energy system, a low‐carbon optimal scheduling strategy for integrated energy systems (IESs) based on improved carbon emission flow and green certificate‐carbon joint trading is proposed. Firstly, based on the multi‐energy coupling characteristics of IES, the impacts of renewable energy generations (REGs), multi‐energy coupling components, and energy storage devices on the carbon flow distribution are quantified, and an improved carbon flow calculation method for IES is proposed to solve the tracing problem of the temporal and spatial transfer of carbon emissions caused by the power loss of coupling components and the operation of energy storage. Then dynamic carbon emission factor reflecting the temporal and spatial distribution characteristics is proposed in combination with the concept of carbon potential. Moreover, to deeply explore the potential of source‐load synergy for carbon reduction, a green certificate‐carbon joint trading mechanism is constructed on the power generation side by combining the green certificate carbon reduction mechanism and carbon trading, and on the load side, the dynamic carbon emission factor is used as the carbon price correction coefficient for energy consumption prices, driving users to respond synergistically to carbon‐energy. Finally, by combining the scheduling of the source‐side units with the demand response on the load side, a bi‐level optimal scheduling model considering the deep reduction of carbon by source‐load synergy is constructed. The simulation analysis of the case study shows that the proposed method achieves carbon measurement, carbon tracing, and reasonable allocation of carbon emission responsibilities in the IES, effectively improving the system's carbon reduction capacity and economic benefits.

The integrated energy system emphasizes the synergy between multiple energy sources compared with the traditional distributed energy subsystem. At present, the research on integrated energy systems is mostly the optimization research of single independent systems, which fails to give full play to the advantages of integrated energy systems. Since the mutual coupling between the electro-gas-thermal networks makes the multi-region integrated energy system optimization operation more complicated, the current research is still immature and needs further research. This paper firstly models the energy storage (storage, heat storage, gas storage) equipment and energy coupling equipment in the integrated energy system. Secondly, it establishes the objective function that considers the minimum system cost and establishes the power of the grid, and give the constraints of power balance equation and line loss equation of power grid, heat network and gas network; Finally, the simulation results show that after the grid, the heat network and the gas network are combined, the original distribution system is converted into a joint supply system through the grid, the heat network and the gas network, which improves the overall economy.

Demand response, as an effective dispatching method, has been widely used in the optimization research of integrated energy system (IES). In this article, first, the mathematical model of flexible loads including shiftable electric, heat, and cooling load are established in line with their characteristics. Second, an optimal scheduling model of the IES is built, where the minimum sum of system operation and maintenance and the environmental protection costs comprise the objective function. Furthermore, the alternating direction multiplier method is used to solve the optimal scheduling model in a fully distributed manner. Finally, simulation results show that the proposed model and algorithm can efficiently solve the IES optimal dispatching problem, reduce the peak-valley difference, and improve the stability of the system.

With the deepening of multi-energy flow coupling and the diversification of energy utilization demand, integrated energy system (IES) puts forward higher requirements for the economy and reliability of capacity allocation scheme. However, there is a complex relationship between economy and reliability, which makes it difficult for them to achieve coordinated optimization. Therefore, an economic and reliability optimal capacity allocation method of IES based on improved co-evolution algorithm is proposed. Firstly, the timing scenario reduction method of “wind power-electrical load” based on ordered clustering algorithm and K-means clustering algorithm is proposed. Aiming at minimizing the expected annual cost of construction investment, operation and maintenance, carbon emission and energy purchase cost, and constrained by equipment operation and energy flow balance, a multi-scenario economic operation model is constructed. Considering the dissatisfaction degree of comprehensive energy consumption, the energy supply reliability evaluation model based on Markov chain Monte Carlo (MCMC) method is established. On this basis, the IES multi-objective optimal capacity allocation model is established. Further, based on the orthogonal test method, the influence factors of decision variables on IES economy and reliability are calculated, and the evolution subpopulation of economy and reliability is established, and then the optimization model’s solution method based on improved co-evolution algorithm is proposed. Finally, an example simulation analysis is carried out combined with the actual data of a certain region. The simulation results show that the proposed model can cooperatively improve the economy and reliability of IES capacity allocation scheme, and provide a reference for the optimal capacity allocation of equipment in IES.

To tackle the issue of operational flexibility degradation of integrated energy system (IES) due to the random charging behavior of massive electric vehicles (EVs), a chance constrained programming (CCP)-based optimal dispatch model of IES with EVs is proposed to ensure the economical and efficient operation. First, an optimal dispatch model with the objective of minimizing system operation cost and environmental protection cost is constructed, and the system operation constraints and the chance constraint that the random charging power of EVs does not exceed their standby capacity are integrated to improve the economic and environmental benefits of IES. Then, the above chance constraint is transformed into a deterministic equivalence class according to a predefined confidence level based on the deterministic equivalence method of chance constraint. Further, to overcome the problem of high complexity of the chance constrained transformation programming model, the alternating directional multiplier method (ADMM) with good convergence is used to obtain high-quality solutions. Finally, simulation results show that the method enable effective response to the randomness of EVs charging behavior and improve the economy and flexibility of system operation.

Integrated Energy Systems (IES), which leverage the synergistic coordination of electricity, heat, and gas networks, serve as crucial enablers for a low-carbon transition. Current research predominantly treats energy storage as a subordinate resource in dispatch schemes, failing to simultaneously optimise IES economic efficiency and storage operators' profit maximisation, thereby overlooking their potential value as independent market entities. To address these limitations, this study establishes an operator-autonomous management framework incorporating electrical, thermal, and hydrogen storage in IES. We propose a joint optimal dispatch model for hybrid energy storage systems in low-carbon IES operation. The upper-level model minimises total system operation costs for IES operators, while the lower-level model maximises net profits for independent storage operators managing various storage assets. These two levels are interconnected through power, price, and carbon signals. The effectiveness of the proposed model is verified by setting up multiple scenarios for example analysis.

Integrated energy system (IES) has advantages in improving the comprehensive utilization efficiency of energy, promoting the consumption of renewable energy and solving the problems of energy shortage and environmental pollution. An optimal planning method of IES considering photovoltaic (PV) integration is proposed in this paper. Firstly, considering PV units, the typical structure of IES containing electricity, gas, heat and cold energy is constructed, and the energy conversion and storage equipment are modeled; Then, aiming at the lowest total cost in the planning period, considering the constraints such as power balance, equipment working characteristics and upper limit of investment and construction, an optimal planning model of IES is established; Finally, an example is given to verify the effectiveness of the proposed method, and the effects of PV integration and natural gas price on the planning results are analyzed.

Hydrogen energy plays an increasingly vital role in global energy transformation. However, existing electric–hydrogen coupled integrated energy systems (IESs) face two main challenges: achieving stable operation when integrated with large-scale networks and integrating optimal dispatching code with physical systems. This paper conducted comprehensive modeling, optimization and joint simulation verification of the above IES. Firstly, a low-carbon economic dispatching model of an electric–hydrogen coupled IES considering carbon capture power plants is established at the optimization layer. Secondly, by organizing and selecting representative data in the optimal dispatch model, an electric–hydrogen coupled IES planning model considering the integration of wind, photovoltaic (PV), diesel and storage is constructed at the physical layer. The proposed electric–hydrogen coupling model mainly consists of the following components: an alkaline electrolyzer, a high-pressure hydrogen storage tank with a compressor and a proton exchange membrane fuel cell. The IES model proposed in this paper achieved the integration of optimal dispatching mode with physical systems. The system can maintain stable control and operation despite unpredictable changes in renewable energy sources, showing strong resilience and reliability. This electric–hydrogen coupling model also can integrate with large-scale IES for stable joint operation, enhancing renewable energy utilization and absorption of PV and wind power. Co-simulation verification showed that the optimized model has achieved a 29.42% reduction in total system cost and an 83.66% decrease in carbon emissions. Meanwhile, the simulation model proved that the system’s total harmonic distortion rate is controlled below 3% in both grid-connected and islanded modes, indicating good power quality.

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-, park-, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

In order to improve the level of new energy consumption in the system and utilize the clean and efficient characteristics of hydrogen energy, an integrated energy system (IES) scheduling model considering refined utilization of hydrogen energy and generalized energy storage is proposed. Firstly, the two-stage hydrogen energy utilization model of power-to-gas (P2G) is finely modeled, and the waste heat of the P2G methanation reaction is innovatively coupled with the Kalina cycle to improve the thermoelectric decoupling capability of the combined heat and power (CHP) unit. Secondly, integrated demand response, electric vehicles, and hydrogen-containing multi-source energy storage equipment are used as generalized energy storage resources to cut peaks and fill valleys. Then, on the basis of considering the ladder-type carbon trading mechanism, the IES conventional operation model is constructed with the minimum operating cost of the system as the objective function. Furthermore, considering the source-load uncertainty of IES operation, a multi-energy complementary optimal scheduling model of hydrogen-containing IES based on conditional value-at-risk was established. Through simulation analysis, it can be seen that the proposed model takes into account both economic and environmental benefits and improves the system’s ability to “peak cutting and valley filling” and measure risk levels.

Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources

The integrated energy system has obvious economic, energy-saving and environmental protection characteristics, and is one of the important ways to achieve the carbon peak and carbon neutral strategy. The operation mode of the integrated energy system is very flexible, and the system can obtain better operation performance through scheduling optimization. To fully improve the system performance, this paper proposes a two-stage integrated energy system intraday scheduling optimization method considering equipment nonlinearity. First, to minimize the operation cost and consider the linear characteristic constraints of equipment, a linear programming model for the optimal scheduling of the intraday economy is established. Then, based on the linear optimization results and considering the nonlinear characteristics of equipment, a nonlinear optimal scheduling model is established, and the nonlinear optimization method is used to further improve the optimization accuracy of the load setting values of each equipment. Simulation results show that, compared with linear scheduling optimization, the deviation between the scheduling results and the actual operation of the system is reduced after considering nonlinear optimization, thus improving the economy, energy conservation, and environmental protection of the system.

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.