Abstract:The hydrogen energy storage system (HESS) integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty. To address the above issues, a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems (CIESs) with power to hydrogen and heat (P2HH) capability. It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads. First, the refined operation model of HESS is established, and its operation model is linearized according to the operation domain of HESS, which simplifies the difficulty of solving the optimization problem under the premise of maintaining high approximate accuracy. Next, considering the flexible start-stop of alkaline electrolyzer (AEL) and the avoidance of multiple energy conversions, the operation sequences of HESS are formulated. Finally, a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established, and the model is simulated and verified using the source-load prediction data of typical days in each season. The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14% while maintaining similar operating cost to the optimal day-ahead economic optimization scheduling. Furthermore, by formulating the operation sequences of HESS, the operating cost of CIES is reduced by up to about 4.4%.

Abstract:The gradual replacement of gasoline vehicles with electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) in recent years has provided a growing incentive for the collaborative optimization of power distribution network (PDN), urban transportation network (UTN), and hydrogen distribution network (HDN). However, an appropriate collaborative optimization framework that addresses the prevalent privacy concerns has yet to be developed, and a sufficient pool of system operators that can competently operate all three networks has yet to be obtained. This study proposes a differentiated taxation-subsidy mechanism for UTNs, utilizing congestion tolls and subsidies to guide the independent traffic flow of EVs and HFCVs. An integrated optimization model for this power-hydrogen-transportation network is established by treating these vehicles and the electrolysis equipment as coupling bridges. We then develop a learning-aided decoupling approach to determine the values of the coupling variables acting among the three networks to ensure the economic feasibility of collaborative optimization. This approach effectively decouples the network, allowing it to operate and be optimized independently. The results for a numerical simulation of a coupled system composed of a IEEE 33-node power network, 13-node Nguyen-Dupuis transportation network, and 20-node HDN demonstrate that the proposed learning-aided approach provides nearly equivalent dispatching results as those derived from direct solution of the physical models of the coupled system, while significantly improving the computational efficiency.

Abstract:The variable and unpredictable nature of renewable energy generation (REG) presents challenges to its large-scale integration and the efficient and economic operation of the electricity network, particularly at the distribution level. In this paper, an operational coordination optimization method is proposed for the electricity and natural gas networks, aiming to overcome the identified negative impacts. The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants. A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption. This method allows for the full consideration of unbalanced operational scenarios. Meanwhile, the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines. The sequential symmetrical second-order cone programming (SS-SOCP) method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process. The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network. The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost, the curtailment of installed REG, the voltage imbalance of three-phase power system, and the overall carbon emissions.

Abstract:Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems (IESs). Although the optimal scheduling of IES has been widely studied, few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale. Combined with hybrid energy storage, the comprehensive use of various uncertainty optimization methods under different time scales will be promising. This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties. Firstly, the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established. Then, an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage. On this basis, a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min, respectively. The results show that ① the proposed method improves the economic benefits, and the intra-day and real-time scheduling costs are reduced, respectively; ② by adjusting the uncertainty budget in the model, a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved; ③ reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions, thus reducing the cost of system scheduling.

Abstract:To address the strong thermoelectric coupling of the combined heat and power (CHP) units, the low utilization rate of energy storage, and the underexploitation of load-side resource flexibility in integrated energy systems (IESs), this paper proposes an optimal scheduling model of an IES in low-carbon communities considering flexibility of resources and the segmental control of solid oxide fuel cells (SOFCs). Firstly, by replacing the gas turbine (GT) in the CHP unit with an SOFC array to reduce carbon emissions and simultaneously weakening the thermoelectric coupling of the CHP unit, the segmental control method is used to control the SOFC array to improve the overall efficiency of the CHP unit. Secondly, coupled interactions among different types of energy storage equipments are mobilized through the integrated energy storage system to make full use of the remaining space in the heat and natural gas storage tanks. Finally, load-side flexible resources are utilized by considering transferable, substitutable, and heat loads, taking into account the thermal inertia of the building and categorizing rooms based on floors, orientations, and room area. Additionally, different user characteristics are characterized, and the flexible resources of building heating periods in northern cities in China are tapped in depth according to the actual factors. Compared with the traditional model, the optimal scheduling model proposed in this paper can reduce the wind abandonment rate and the carbon emission of community-integrated energy system (CIES) by 4.54% and 70.63%, respectively, and increase the utilization rate of heat and natural gas storage tanks by 12.34% and 30.52%, respectively, and lower the total cost by ¥2183.6 under the premise of ensuring user comfort during energy consumption, which promotes the economic and low-carbon operation of the CIES.

Abstract:The increasing focus on carbon neutrality has led to heightened interest in multiple microgrids (MGs) due to their potential to significantly reduce emissions by the integrated electricity-heat-carbon sharing among them. In this paper, a decentralized peer-to-peer (P2P) framework for integrated electricity-heat-carbon sharing is proposed to optimize the trading process of multi-energy and carbon among multiple MGs. The proposed framework considers certified emission reductions (CERs) of photovoltaic (PV) systems in each MG, and carbon allocation and trading among multiple MGs. The P2P trading behaviors among multiple MGs are modelled as a non-cooperative game. A decentralized optimization method is then developed using a price-based incentive scheme to solve the non-cooperative game and optimize the transactions of the electricity-heat-carbon jointly. The optimization problem is solved using sub-gradient in a decentralized manner. And the Nash equilibrium of the non-cooperative game is proven to exist uniquely, ensuring the convergence of the model. Furthermore, the proposed decentralized optimization method safeguards the private information of the MGs. Numerical results show that the total operational cost of the MGs and the carbon emissions can be reduced significantly.

Abstract:Distributed secondary control has been proposed to maintain frequency/voltage synchronization and power sharing for distributed energy sources in AC microgrids (MGs). The cyber layer is susceptible to time delays and cyber failures and thus, a distributed resilient secondary control should be investigated. This paper proposes a distributed multi-scale attention and predictor-based control (DMAPC) strategy to address false data injection attacks and packet loss failures with time delays. The multi-scale attention mechanism enables the system to selectively focus on neighbors ’ states with higher confidence evaluated in different time scales, while the data-driven predictor compensates for lost neighbors’ states in the nonlinear controller. The DMAPC does not impose strict limitations on the number of false communication links or upper bound for false data. Besides, the DMAPC is formulated as an uncertain system with time delays and is proven to be uniformly ultimately bounded. Extensive experiments on a hardware-in-the-loop MG testbed have validated the effectiveness of DMAPC, which successfully relaxes restrictions on cyber failures compared to existing strategies.

Abstract:Feeder routing and reliability assessment are essential for effective distribution network planning. However, excessively long feeders can lead to increased costs and decreased reliability. To enhance economic and reliability performance, this paper proposes a reliability-centered planning method for feeder routing and conductor sizing. Specifically, a graph-based fictitious power flow model is constructed within the geographic graph. Overlapping feeder routes powered by fictitious power flows from multiple sources are designated as line connection. These feeder routes, constrained by the geographic graph, are interconnected via line connection to form a mesh network structure. To meet the requirements of reliability-centered optimization, the affiliation variables are introduced. Based on the affiliation variables, the algebraic formula is embedded into the fictitious power flow model to enable the calculation of reliability during the optimization process. By incorporating customized reliability-related constraints in the model, the specific reliability objectives can be achieved. In addition, the non-convex terms in the fictitious power flow model are relaxed into convex forms, and certain variable products are replaced with auxiliary variables, allowing the problem to be solved by an off-the-shelf solver. Finally, the proposed method is tested on two case studies, demonstrating its effectiveness.

Abstract:The operation of integrated energy systems (IESs) is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms. Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data. This paper proposes a novel representative scenario generation method based on the feature extraction of panel data. The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation. Then, the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids. Together with the proposed scenario generation method, an optimal operation model of IES is established, where the objective is to minimize the annual operation costs considering carbon trading cost. Finally, case studies based on the data of Aachen, Germany in 2019 are performed. The results indicate that the adjusted rand index (ARI) and silhouette coefficient (SC) of the proposed method are 0.6153 and 0.6770, respectively, both higher than the traditional methods, namely K-medoids, K-means++, and density-based spatial clustering of applications with noise (DBSCAN), which means the proposed method has better accuracy. The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%, while the calculation time is reduced from 11029 s to 188 s, which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.

Abstract:In this paper, a novel multi-objective optimization model of integrated energy systems (IESs) is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies. First, the carbon emission trading mechanism is introduced into the optimal scheduling of IESs, and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system. Then, according to different response characteristics of electric load and heating load, a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode. On these basis, a multi-objective optimization model of IESs is established, which aims to minimize the total operating cost and the renewable energy source (RES) curtailment. Finally, based on typical case studies, the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.

Abstract:To improve the economic efficiency of urban integrated energy systems (UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system (TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers (BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator (ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation (C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

Abstract:This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system (RIES). Our model regards the distribution network and each energy hub (EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic (PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers (ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

Abstract:We propose an optimal stochastic scheduling strategy for a multi-vector energy complex (MEC), considering a full-blown model of the power-to-biomethane (PtM) process. Unlike conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane, a detailed PtM model is introduced to emphasize the reactor kinetics and chemical equilibria of methanation. This model crystallizes the interactions between the PtM process and MEC flexibility, allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic scenarios. Temperature optimization and flowsheet design of the PtM process increase the average selectivity of methane (i.e., ratio between net biomethane production and hydrogen consumption) up to 83.7% in the proposed synthesis flowsheet. Simulation results can provide information and predictions to operators about the optimal operating conditions of a PtM unit while improving the MEC flexibility.

Abstract:Adopting high penetration levels of electric vehicles (EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to ① the lack of utility-grade communication systems in many cases such as secondary (low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and ② existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators (DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.

Abstract:The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system (IES). Therefore, this study proposes a state transition modeling method for an IES based on a cyber-physical system (CPS) to optimize the state transition of energy unit in the IES. This method uses the physical, integration, and optimization layers as a three-layer modeling framework. The physical layer is used to describe the physical models of energy units in the IES. In the integration layer, the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model, and the transition conditions between different states of the energy unit are given. The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state. Numerical simulations show that, compared with the traditional modeling method, the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle, which obtains an optimal state of the energy unit and further reduces the system operating costs.

Abstract:A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems (RIESs) with multiple stakeholders. A two-level Stackelberg game model is established, with the RIES operator as the leader and the users as the followers. It considers the interests of the RIES operator and demand response users in energy trading. The leader optimizes time-of-use (TOU) energy prices to minimize costs while users formulate response plans based on prices. A two-stage distributionally robust game model with comprehensive norm constraints, which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage, is constructed to manage wind power uncertainty. Karush-Kuhn-Tucker (KKT) conditions transform the two-level Stackelberg game model into a single-level robust optimization model, which is then solved using column and constraint generation (C&CG). Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders’ interests and mitigating wind power risks.

Abstract:Decarbonization in the power sector is one of the critical factors in achieving carbon neutrality, and the top-level design needs to be carried out from the perspective of power planning. A multi-stage provincial power expansion planning (PPEP) model is proposed to simulate the power expansion planning at different stages of the power systems rich in renewable energy generation. This model covers 16 types of power supply, considering macro-policy demands and micro-operation constraints. The stand-alone capacity aggregation model for coal-based units within the PPEP model allows for accurate construction and retirement with different stand-alone capacities. Moreover, the soft dynamic time warping (soft-DTW) based K-medoids technique is adopted to generate typical scenarios for balancing the model accuracy and solution efficiency. Additionally, a multi-market trading equilibrium (MMTE) mechanism is proposed to address the differences in the levelized cost of energy between the coal-based and renewable-based units by participating in energy and ancillary service markets. Since the coal-based units take on the task of providing ancillary services from renewable-based units in the ancillary service market, the MMTE mechanism can effectively equalize the profits of both by having renewable-based units purchase ancillary services from coal-based units and pay for them, thus improving the motivation of coal-based units. A case study in Xinjiang province, China, verifies the effectiveness of the planning results of the PPEP model and the profit equilibrium realization of the MMTE mechanism.

Abstract:Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strengthened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.

Abstract:To improve the safety of the solid oxide fuel cell (SOFC) systems and avoid the generation of large amounts of pollutants during power switching, this paper designs a power switching strategy based on trajectory planning and sliding mode control (TP-SMC). The design elements of the power switching strategy are proposed through simulation analysis at first. Then, based on the gas transmission delay time and the change of gas flow obtained from testing, trajectory planning (TP) is implemented. Compared with other power switching strategies, it has been proven that the power switching strategy based on TP has significantly better control performance. Furthermore, considering the shortcomings and problems of TP in practical application, this paper introduces sliding mode control (SMC) on the basis of TP to improve the power switching strategy. The final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay time. Compared with TP, TP-SMC can ensure that under uncertain conditions, the SOFC system does not experience fuel starvation and temperature exceeding limit during power switching. Meanwhile, the NOx emissions are also within the normal and acceptable range. This paper can guide the power switching process of the actual SOFC systems to avoid safety issues and excessive generation of NOx, which is very helpful for improving the performance and service life of the SOFC systems.