Analytical Target Cascading Research Articles

Evaluation and maintenance method for general speed railway turnouts based on multi-source data

In recent years, with the increasing passengers' requirements for safety and comfort, as well as the measures of increasing revenue and reducing the expenditure of railway company, more efficient, more scientific and more comprehensive requirements have been put forward for the maintenance of railway turnouts. This paper develops a maintenance strategy based on the Turnout Service State Indicator (TSSI), derived from an analysis of multi-source data concerning turnout conditions. The weights of critical indicators affecting turnout conditions are determined using Analytical Target Cascading (ATC) and Analytic Hierarchy Process (AHP). Subsequently, turnouts are classified into four categories—great, good, general, and poor—based on their TSSI values, guiding specific maintenance strategies. The proposed assessment method was validated using data from four sets of turnouts collected on actual tracks, showing consistency between the assessment results and field technicians' evaluations. A comparative analysis of the ATC and AHP suggests that the latter provides a more scientific and objective assessment of turnout conditions. The established methods for evaluating the service state and deciding on turnout maintenance actions have been proven feasible and practical through field validation. These methods can be broadly applied to maintain general speed railway turnouts to enhance repair efficiency and reduce maintenance costs.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Economic optimal dispatch of active distribution network with CCHP multi-microgrid based on analytical target cascading

When multiple CCHP microgrids are integrated into an active distribution network (ADN), the microgrids and the distribution network serve as distinct stakeholders, making the economic optimal dispatch of the system more complex. This paper proposes a distributed dispatch model of ADN with CCHP multi-microgrid, and refines the objective functions of each region. The analytical target cascading approach (ATC) is employed to model the power transaction as virtual sources/loads, and solve the optimal dispatch in parallel. Case studies demonstrate the proposed distributed model is capable of achieving economic optimization for both stakeholders.

Distributed optimization for EVs integrated power system considering flexible ramping requirement

More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs’ dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively.

Trajectory/propulsion integrated design optimization of the manned lunar lander propelled by hybrid rocket motors using analytical target cascading

Lunar missions are currently experiencing a significant surge in popularity, presenting expansive opportunities for further exploration and development. To thoroughly explore the design margins and potential of lunar landers, and to foster the development of overall designs driven by comprehensive performance objectives, it is crucial to conduct optimization design considering the coupling between key disciplines such as trajectory and propulsion. Considering the significant increase in computational complexity caused by conducting trajectory/propulsion integrated design optimization, the analytical target cascading method is employed to hierarchically decompose and coordinate optimization of the complex systems. This article presents a phased soft-landing strategy on the manned lunar lander propelled by hybrid rocket motors, utilizing powered explicit guidance and Apollo powered descent guidance, and proceeds with the trajectory/propulsion integrated design optimization involving diverse grain shapes and feed systems. This optimization process is separately undertaken utilizing multidisciplinary feasible method and analytical target cascading method. The analysis reveals that integrating trajectory and propulsion considerations into the optimization process facilitates a 5 % reduction in the overall mass relative to optimizations constrained solely by velocity increment and lack comprehensive trajectory design considerations. This highlights the profound impact of trajectory requirements on propulsion system design and the advantages of powered explicit guidance laws in minimizing fuel consumption. Crucially, the use of analytical target cascading achieves the better optimization results, and significantly reduces subsystem evaluation times, enhancing operational efficiency by 48 %, demonstrating the advantage in handling complex, large-scale systems. On another level, with different β values, the Mean Relative Error of the target values for the three schemes obtained by the analytical target cascading method is 0.0016, indicating good stability and strong robustness. The practical exploration in this article provides methods and frameworks for high-performance optimization design of complex aerospace mission profiles in the future.

Analytical target cascading based real-time distributed voltage control for MV and LV active distribution networks

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

Intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems with wind power integration

To make full use of the flexible adjustment capability of DC tie-lines in multiarea AC/DC systems and to coordinate the generation resources and load demand of multiarea AC/DC systems, this paper presents an intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems based on analytical target cascading (ATC). To avoid repeated adjustment and overadjustment of DC tie-lines after wind power integration, a two-stage rolling coordinated scheduling model for the area subsystem based on model predictive control (MPC) is proposed. The two-stage rolling coordinated scheduling method takes into consideration the influence of the predicted value in the future finite time domain and the latest measured DC tie-line power on the current scheduling state. Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed that takes into consideration the DC tie-line adjustment constraints and the area coupling constraints. The optimal scheduling model for the upper-level system formulates the two-stage DC tie-line plan for the multiarea AC/DC system, and the two-stage rolling coordinated scheduling model of the area subsystem solves the subproblems of the generation plan for each area subsystem in a parallel manner considering the area coupling constraints. The proposed method can achieve intraday two-stage decoupling scheduling of multiarea AC/DC systems and promote the cross-area consumption of large-scale wind power through flexible adjustment of the DC tie line. This approach also reduces the communication burden between the area subsystems and ensures the efficiency of the solution algorithm.

Collaborative Control of Reactive Power and Voltage in a Coupled System Considering the Available Reactive Power Margin

This article proposes a hierarchical collaborative reactive power and voltage control method aimed at meeting the internal voltage requirements of a coupled system composed of centralized new energy stations such as wind and solar power and traditional thermal power units, with particular attention paid to the reactive power regulation ability of power units in new energy stations. This method is implemented in two layers: At the transmission layer, the overall reactive power compensation capacity of thermal power units and stations on the transmission lines within the system is determined according to the engineering requirements of different node voltages in the coupled system, the regulation voltages of the high-voltage grid-connected points and the point of common coupling (PCC) of the stations are found, and the actual regulation voltages of the PCCs of the stations are given as commands. At the station layer, based on determining reactive power distribution between power units and continuous reactive power compensation devices within each station, voltage optimization is carried out using the voltage command issued by the transmission layer as a reference. At the same time, consistency coordination between the transmission layer and the station layer is achieved based on the Analytical Target Cascading (ATC) method at different layers. Finally, simulation analysis was conducted on a regional coupled system in Dalian. The results showed that the method proposed in this paper can effectively control the voltage safety requirements of each region and PCC in the coupled system and, to some extent, improve the economic efficiency of system operation.

Energy management of multi-microgrid system with renewable energy using data-driven distributionally robust optimization

ABSTRACT By clustering multiple microgrids (MGs), a multi-microgrid (MMG) system plays a significant role in integrating a large amount of renewable generation. However, the large-scale utilization of renewable energy also brings uncertainties to MMG energy management. In this work, a novel comprehensive bi-level MMG energy management model considering uncertainties of renewable energy is first developed which includes unit commitment (UC) problem and other models in the lower MG level. Then considering the non-convexity of the bi-level problem, a decomposition method called analytical target cascading (ATC) is employed to deal with it by decentralization. With regard to the energy management of each individual MG, a two-stage distributionally robust model is developed which describes the uncertainties from probability distribution of renewable generation with a metric-based ambiguity set. Moreover, an efficient solution scheme based on column and constraint generation (CCG) algorithm and a decomposition method without duality is designed to handle the MG energy management problem. Finally, we implement extensive experiments to corroborate the effectiveness of the proposed approach. Particularly, the optimal solution from the proposed method can attain 2.98% less cost compared with that from robust optimization (RO) method and achieve 86.17% less energy trading amount than that in independent mode.

A decentralized dispatch model for multiple micro energy grids system considering renewable energy uncertainties and energy interactions

Micro energy grids (MEGs) play a vital role in realizing carbon neutrality and efficient utilization of renewable energy resources. This research focuses on optimizing the synergy of MEG interconnections. Given the diverse development paths of different operating entities within the system, information barriers emerge among MEGs, creating great difficulties for the collaborative system management. In response, this paper proposes a decentralized coordinated dispatch model targeting multiple stakeholders within the system. This model accounts for energy interactions between MEGs and the inherent uncertainty associated with renewable energy sources. Specifically, stochastic optimization approach was applied to characterize the uncertainty of renewable energy output by generating stochastic scenarios. Furthermore, it incorporates the analytical target cascading (ATC) method to decouple objective functions and constraints, creating autonomous scheduling sub-models for individual MEGs. This decentralized approach ensures independent modeling and coordinated problem-solving. Simulations verify that (1) the ATC-based inter-MEG energy interaction strategy effectively achieves decentralized coordinated scheduling of multiple MEGs and (2) the decentralized coordinated scheduling solution closely approximates the global optimum while considering the interest of various system entities.

Distributed distributionally robust optimization of distribution network incorporating novel battery charging and swapping station

This paper addresses the joint optimization problem of the distribution network (DN) and novel battery charging and swapping (NBCSS) under multi-entities and uncertainties. A distributed distributionally robust optimization (DRO) framework is proposed to facilitate the coordination of DN and NBCSS. Firstly, the DRO models for the DN and NBCSS are developed respectively for the DN and NBCSS, taking into account the uncertainties associated with wind and photovoltaic power. Both models minimize the worst-case expected total cost over a family of distributions characterized by an ambiguity set. Then, the constraints of exchanging power are used to couple the DN and NBCSS. Specifically, in the model of NBCSS, an integrated modeling method of batteries based on the state of charge (SOC) interval is constructed, and the charging and discharging priorities are embedded. Further, the primitive DRO model is transformed into a tractable mixed-integer linear program (MILP) via the affine decision rule and duality theory. Moreover, to protect the information privacy of different entities, the analytical target cascading (ATC) method is developed to decouple the centralized DRO model into two independent subproblems, which can be solved separately. Finally, case studies are carried out to demonstrate the effectiveness of the proposed method.

A multi-layer scheduling framework for transmission network overload alleviation considering capabilities of active distribution networks

As distributed generations and flexible loads are widely connected to distribution networks, traditional distribution networks become “active”, and the response of the active distribution network (ADN) to the transmission network is receiving increasing attention. In this paper, the congestion problem of transmission network is studied from a demand-side perspective, and a multi-layer scheduling framework considering capabilities of ADNs is developed to reduce unnecessary load shedding operations in transmission network. The proposed framework consists of three layers. In the first layer, ADN’s power flow is linearized by the second-order cone programming, based on which a reactive power optimization scheduling model is constructed to minimize network loss in ADNs. When the network loss reduction in ADN cannot meet the requirements for congestion mitigation, a bi-level self-cycle model based on analytical target cascading is constructed in the second layer, which contributes to dispatch distributed generators’ outputs and further curtail load from the ADN side. When the second layer fails to satisfy the required load curtailment, a demand response model considering user acceptance and rejection is proposed to obtain the optimal shedding strategy for interruptible loads, and ultimately alleviate the overload problem. A case study with varying degrees of overload in the transmission network is conducted to demonstrate the proposed framework.

Coordinated Planning of Fixed and Mobile Charging Facilities for Electric Vehicles on Highways

This paper presents a bilevel planning framework to coordinate truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to promote charging flexibility and provide more choices for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed at the upper level to optimize the location of charging stations. Furthermore, a collaborative capacity optimization (CCO) approach is formulated at the lower level to optimize the capacity of TMC and FC at candidate stations. In the proposed framework, origin-destination (OD) analysis, Floyd algorithm and Monte Carlo simulation (MCS) are employed to generate the spatial-temporal distribution of charging demand based on historical data. An improved income approach (IIA) is then developed to well capture the heterogeneity of EV users’ charging behavior. The waiting cost of EV users is estimated by their value of time (VOT), which helps them to make a better choice between TMC and FC. To solve the optimal model easily, the big-M method is applied to linearize and convert the nonlinear problem into a mixed-integer linear programming (MILP) model. Meanwhile, the analytical target cascading (ATC) technique is employed to realize the data exchange process between the upper and lower layers. Finally, numerical study demonstrates the effectiveness of the proposed framework and method.

Optimal transmission switching for power system integrating renewable energy based on analytical target cascading

The penetration rate of intermittent renewable energy in power system is gradually increasing, which brings challenges to the optimal dispatch of power system. To solve these problems, a multi-regional interconnected transmission network optimization method based on analytical target cascading is proposed. Firstly, the reactive power regulation characteristics of renewable energy is investigated, and the models of wind turbine and photovoltaic generation are established. Secondly, the power system is decomposed into multiple sub-systems using bus tearing method, and an optimal transmission switching (OTS) model with renewable energies is established. Then, the analytical target cascading (ATC) approach is employed to decompose the model into the main problem and several sub-problems for parallel computation to achieve coordinated optimization of the complex transmission network. Finally, the IEEE 14-bus system and the IEEE 118-bus system are used to verify the proposed model. The results show that the proposed method can deal with the coupling nonlinear problem well and promote the consumption of renewable energy.

Fully parallel decentralized load restoration in coupled transmission and distribution system with soft open points

Collaboration between transmission and distribution system operators (TSOs and DSOs) is crucial to improve power supply reliability during a massive blackout. However, these two systems usually operate separately in reality. To coordinate them, a new fully parallel decentralized TSO + DSO service restoration (D-TDSR) scheme is proposed in this paper. Based on analytical target cascading (ATC) method, the proposed D-TDSR is able to formulate the local restoration model for each TSO and DSO by decomposing shared boundary information in an iterative process. By introducing the diagonal quadratic approximation in ATC, all local restoration models are solved in a fully parallel manner with no need for a central coordinator. This parallel implementation increases the computation efficiency of decentralized restoration procedure. Furthermore, to enhance the ability of TSO and DSOs to support each other, the potential benefits of flexible regulation in DSO, including soft open points and network reconfiguration, are fully exploited, thus improving critical load restoration speed, and reducing voltage deviations. The effectiveness of the proposed D-TDSR is validated using an IEEE standard test system.

Collaborative Optimization of Transmission and Distribution Considering Energy Storage Systems on Both Sides of Transmission and Distribution

With the high penetration of renewable energy resources, power systems are facing increasing challenges in terms of flexibility and regulation capability. To address these, energy storage systems (ESSs) have been deployed on both transmission systems and distribution systems. However, it is hard to coordinate these ESSs with a single centralized optimization, and the time-domain coupling constraints of ESSs lead to high optimization complexity and a time-consuming calculation process. In this regard, this paper proposes a hierarchical transmission and distribution systems coordinative optimization framework, considering the ESSs at both ends of the systems. The decoupling of the time-domain coupling constraints of ESSs is realized by the Lyapunov optimization. Furthermore, the decoupling mechanism is embedded in the iterative process of analytical target cascading (ATC). In addition, an ATC-based Lyapunov optimization (ATC-L) approach is proposed to solve the co-optimization problem of the operations of the transmission system with multiple connected distribution systems. Through a case study, it is verified that the proposed framework and the ATC-L approach can effectively reduce the system’s operational cost and improve the consumption rate of renewable energy.

A parallel approach with the strategy-proof mechanism for large-scale group decision making: An application in industrial internet

The consensus-reaching process (CRP) is essential for forming a solution in large-scale group decision-making (LSGDM). We designed a parallel method with a strategy-proof mechanism to support CRP in the LSGDM. First, considering the previous clustering methods’ poor performance in non-convex datasets, a density-based clustering method (DBCM) is proposed. Because the parameters of DBCM influence the performance of clustering, they are optimized based on the CRP. Second, after clustering, the analytical target cascading (ATC) method is proposed to support CRP. For ATC, we set the moderator as the first layer and subgroups as the second layer. Each subgroup connects only to the moderator and realizes the consensus separately. The final consensus is realized when the difference among the subgroups’ alternatives is lower than a threshold value. The ATC method supports the high-efficiency, independent, and distributed CRP in LSGDM, which is feasible in new situations prompted by COVID-19, like telecommuting, shared manufacturing, cloud-based medical treatment, and distributed designing. To enhance the efficiency of CRP, we propose a preference learning method based on big data. Third, a strategy-proof mechanism is proposed to prevent manipulation in LSGDM, which indicates that the expert’s profit is higher than that of the expert with manipulation, regardless of the manipulating possibility of experts. The mechanism avoids the loss caused by experts’ manipulation in the CRP. Finally, we design an enhanced gray wolf algorithm to solve the optimization problem. The advantages of the proposed method are verified by the slewing bearing design in the industrial internet.

Robust Hierarchical Scheduling Approach to Coordinate Wind Power Penetrated Transmission System and LNG Railway Transportation

Rail transport of liquefied natural gas (LNG), as an essential means of transmitting energy across regions, has been widely implemented and promoted. The significant growth of gas-fired power and power-to-gas units has intensified the interaction and interdependency between wind power penetrated transmission system and LNG transported railway network. Hence, a robust and hierarchical scheduling approach to coordinate the transmission system and LNG railway transportation is innovatively proposed. First, an advanced time-space network (TSN) model is established with additional security constraints to exactly simulate the railway traffic. Then, a two-stage robust scheduling model is formulated with an uncertainty set incorporating random components failures of the transmission system and the railway network as well as variability of wind power and loads. Subsequently, the two-stage robust scheduling problem is decomposed into two interactive levels according to the analytical target cascading (ATC) technique, thus ensuring the information privacy and decision independency of the power and railway systems. Finally, an ATC-based hierarchical solution framework embedded with the column-and-constraint generation (C&CG) algorithm is presented to solve the proposed model. Case studies demonstrate the effectiveness and benefit of collaboratively managing the power and railway systems.

Decentralized and coordinated scheduling model of interconnected multi-microgrid based on virtual energy storage

The joint coordinated optimization scheduling of multi-microgrid can reduce the risk of accommodation of renewable energy and the operation costs. In this paper, a decentralized and coordinated scheduling method of interconnected multi-microgrid based on virtual energy storage is proposed. Firstly, the microgrid is modelled as a virtual energy storage model, and the demand and supply are packaged into the charge and discharge states of the virtual energy storage model for information privacy of microgrid. Secondly, the probability characteristics of renewable energy prediction error and system flexible resource are used to quantify the operation risk of microgrid and the available charge and discharge power. The decentralized and coordinated scheduling method contains two-levels optimization model to optimize the power of tie-line and virtual energy storage. The upper is the central coordinate controller scheduling model, and the lower is the optimization scheduling model of sub-microgrid. The analytical target cascading is used to decouple the established upper and lower level models for independent optimization and solution. The proposed scheduling model ensures independent operation of each microgrid, and realizes decentralized autonomy and centralized coordinated management of multi-microgrid. Case studies demonstrate the correctness and a well economy of the proposed scheduling method.

A set‐based approach for hierarchical optimization problem using Bayesian active learning

AbstractIn the development of complex engineering systems, many engineers from different disciplines collaborate to identify feasible designs that satisfy all system requirements. Analytical target cascading (ATC) is a method for the design optimization of hierarchical, multilevel systems and has been successfully employed in the design of complex engineering systems. In this paper, we propose a novel data‐driven set‐based ATC (SBATC) method for hierarchical design optimization problems using machine learning techniques. The proposed SBATC offers two advantages in engineering processes. First, it decomposes hierarchical design optimization problems into sets of suboptimization problems. The feasible regions of the suboptimization problems are explored and cascaded to lower‐level optimization problems. Using the set‐based approach, couplings between two adjacent levels in the optimization process are not required. Second, an efficient strategy is employed to determine feasible regions based on Bayesian active learning (BAL). In BAL, the Gaussian process (GP) of all cost functions is trained. An acquisition function that combines the probability of feasibility and entropy search is evaluated using posterior distributions of the trained GP. The acquisition function is maximized to generate new sampling points around the feasible regions by balancing the exploitation and exploration of the design space. To verify the effectiveness of the proposed method, numerical examples of hierarchical optimization problems are evaluated.