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Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

APPLIED THERMAL ENGINEERING 2024年 257卷

作者： Wu, Xialai Qin, Jiabin Chen, Junghui Wang, Yongli Huzhou Univ Coll Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Chung Yuan Christian Univ Chung Li R&D Ctr Membrane Technol Taoyuan 320 Taiwan Chung Yuan Christian Univ Chung Li Dept Chem Engn Taoyuan 320 Taiwan

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input-output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

关键词： mixed integer linear programming problem Organic Rankine cycle Predictive control Recurrent neural network

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Online Optimization of Pickup and Delivery problem Considering Feasibility

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FUTURE INTERNET 2024年第2期16卷 64页

作者： Matsuoka, Ryo Kobayashi, Koichi Yamashita, Yuh Hokkaido Univ Grad Sch Informat Sci & Technoloty Sapporo 0600814 Japan

A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents' paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method.

关键词： pickup and delivery problem online optimization mixed integer linear programming problem fuel constraints demand forecasting

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Stochastic scheduling of compressed air energy storage in DC SCUC framework for high wind penetration

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第13期13卷 2747-2760页

作者： Gupta, Pranda Prasanta Jain, Prerna Sharma, Kailash Chand Bhakar, Rohit Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Banasthali Vidyapith Sch Automat Vanasthali Rajasthan India

High intermittent wind generation necessitates integration of bulk energy storage systems (ESSs) for maintaining security and reliability in power system operation. Considering this, stochastic security constrained unit commitment (SCUC) including compressed air energy storage (CAES) as bulk ESS for high wind penetration and with wind uncertainty modelling is addressed. Network constraints for pre- and post-line contingency are modelled using DC power flow. Injection sensitivity factors (ISFs) are conventionally used in power flow equations which, however, make N - 1 network security constrained formulation huge and computationally demanding for the proposed stochastic model. Therefore, this study proposes a line outage distribution factor (LODF) to reduce the number of coefficients of post contingency DC power flow equations. This is a compact formulation with the lower computational requirement. Wind uncertainty is modelled as probable scenarios. The proposed SCUC is a complex mixed integer linear programming problem and solved using Benders decomposition technique for IEEE 30-bus and 118-bus system. Simulation results to analyse the impact of CAES, wind uncertainty and line contingency with ISF and LODF on overall operation costs, CAES scheduling, wind curtailment, locational marginal price and computational time. Results show that the proposed model is computationally efficient for system operation under high wind penetration.

关键词： power generation scheduling power system security power generation dispatch load flow compressed air energy storage stochastic processes linear programming integer programming wind power plants power system reliability high intermittent wind generation bulk energy storage systems power system operation stochastic security constrained unit commitment compressed air energy storage wind uncertainty modelling post-line contingency line outage distribution factor wind curtailment stochastic scheduling DC SCUC framework wind penetration DC power flow equations IEEE 118-bus system N-1 network security constrained formulation injection sensitivity factors ISF LODF mixed integer linear programming problem CAES scheduling Benders decomposition technique IEEE 30-bus system

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Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs

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IET RENEWABLE POWER GENERATION 2019年第7期13卷 1037-1049页

作者： Arasteh, Farzad Riahy, Gholam H. Amikabir Univ Technol Dept Elect Engn Tehran Iran

The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner.

关键词： power generation economics power generation scheduling optimal control Markov processes wind power plants optimisation power generation planning power markets linear programming power generation reliability probability linear matrix inequalities integer programming demand side management simultaneous coordination transmission switching demand response programs wind power generation market-based power systems challenging situation optimal decisions applicable control strategies corresponding control facilities high-priority cheap wind power utilisation transmission lines congestion different solutions TS intermittent wind power generations social welfare maximisation problem coordinated control DR facilities linear matrix inequality parser YALMIP powerful employed solvers practical modelling optimisation toolbox mixed integer linear programming problem wind power uncertainty optimisations large-scale IEEE 118-bus test systems control strategy optimal control commands market based wind integrated power systems

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Robust expansion co-planning of electricity and natural gas infrastructures for multi energy-hub systems with high penetration of renewable energy sources

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IET RENEWABLE POWER GENERATION 2019年第13期13卷 2287-2297页

作者： Karamdel, Shahab Moghaddam, Mohsen Parsa Tarbiat Modares Univ Dept Elect Engn Tehran Iran

High penetration of renewable energy sources will cause crucial challenges for future energy systems. This study presents a three-level model for adaptive robust expansion co-planning of electricity and natural gas infrastructures in multi-energy-hub networks, which is robust against uncertainties of maximum production of wind generation and gas-fired power plants as well as estimated load levels. The proposed min-max-min model is formulated as a mixed integer linear programming problem. The first level minimises the investment cost of electricity and natural gas infrastructures, the worst possible case is determined through the second level, and the third level minimises the overall operation cost under that condition. To solve this model, the final minimisation problem is replaced by its Karush-Kuhn-Tucker conditions and a two-level problem is determined. Finally, by using the column and constraint generation algorithm the original problem is decomposed to master and sub-problems and the optimal solution is derived iteratively. The proposed robust expansion co-planning model is tested on modified Garver's 6-hub, modified IEEE RTS 24-hub, and modified IEEE 118-hub test systems and numerical results show its effectiveness to cope with uncertainties with regard to control conservativeness of the plan.

关键词： load flow renewable energy sources linear programming minimax techniques integer programming power generation planning natural gas technology adaptive robust expansion co-planning electricity natural gas infrastructures multienergy-hub networks wind generation min-max-min model mixed integer linear programming problem final minimisation problem two-level problem robust expansion co-planning model modified IEEE 118-hub test systems multienergy-hub systems high penetration renewable energy sources future energy systems three-level model gas-fired power plants constraint generation algorithm estimated load levels

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Stackelberg game-based energy management for a microgrid with commercial buildings considering correlated weather uncertainties

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第11期13卷 2102-2111页

作者： Wang, Jiaying Feng, Changsen Xu, Yan Wen, Fushuan Zhang, Lijun Xu, Chengbo Salam, Abdus Zhejiang Univ Sch Elect Engn Hangzhou 310027 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore State Grid Zhejiang Econ Res Inst Hangzhou 310008 Zhejiang Peoples R China Univ Teknol Brunei Dept Elect & Elect Engn BE-1410 Bandar Seri Begawan Brunei

This paper proposes a Stackelberg game-based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi-level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush-Kuhn-Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation-based 2M + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method.

关键词： game theory duality (mathematics) distributed power generation HVAC linear programming pricing tariffs energy management systems optimisation integer programming CBs dynamic pricing tariff MGO bi-level optimisation problem mixed integer linear programming problem Karush-Kuhn-Tucker condition correlated solar irradiance solar illuminance uncertainties Nataf transformation-based Stackelberg game-based energy management commercial buildings correlated weather uncertainties Stackelberg game-based optimal energy management model flexible loads air conditioning system lighting system microgrid operator optimal energy management scheme size 2 0 m

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Optimal distribution system restoration using PHEVs

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IET SMART GRID 2019年第1期2卷 42-49页

作者： Sun, Wei Kadel, Nemica Alvarez-Fernandez, Inalvis Nejad, Reza Roofegari Golshani, Amir Univ Cent Florida Elect & Comp Engn Dept Orlando FL 32816 USA Elect Reliabil Council Texas Transmiss Planning Austin TX USA

Power outages cost billions of dollars every year and jeopardise the lives of hospital patients. Traditionally, power distribution system takes a long time to recover after a major blackout, due to its top-down operation strategy. New technologies in modern distribution systems bring opportunities and challenges to distribution system restoration. As fast response energy resources, plug-in hybrid electric vehicles (PHEVs) can accelerate the load pickup by compensating the imbalance between available generation and distribution system load. This study provides a bottom-up restoration strategy to use PHEVs for reliable load pickup and faster restoration process. The optimisation problem of finding load pickup sequence to maximise restored energy is formulated as a mixed integer linear programming (MILP) problem. Moreover, the coordination between transmission and distribution restoration is developed to efficiently restore the entire system back to normal operating conditions. Simulation results on one 100-feeder test system demonstrate the efficiency of MILP-based restoration strategy and the benefit from PHEVs to restore more energy in given restoration time. The proposed restoration strategy has great potential to facilitate system operators to achieve efficient system restoration plans. It also provides incentives to deploy a large amount of PHEVs to improve system resiliency.

关键词： power system restoration linear programming integer programming power distribution faults optimisation hybrid electric vehicles power system reliability normal operating conditions 100-feeder test system MILP-based restoration strategy PHEVs system operators efficient system restoration system resiliency optimal distribution system restoration power outages hospital patients power distribution system operation strategy modern distribution systems fast response energy resources hybrid electric vehicles available generation distribution system load reliable load pickup faster restoration process load pickup sequence restored energy mixed integer linear programming problem restoration time transmission restoration distribution system restoration bottom-up restoration strategy

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Optimizing private and public mode of operation in major ports of India for better customer service

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INDIAN GROWTH AND DEVELOPMENT REVIEW 2019年第1期12卷 2-37页

作者： Sinha, Deepankar Chowdhury, Shuvo Roy Indian Inst Foreign Trade IIFT Kolkata Campus Kolkata India

Purpose The Government of India announced its liberalization policy in the year 1991. Since then, the major ports in India introduced privatization in various forms into their operations. However, the share of total traffic (cargo) handled by major ports fell from 90 per cent in 1991 to around 70 per cent in 2015, losing share to minor ports. These major ports, except for the port of Kamarajar, are governed by the Major Port Trust Act, 1961. None of the Indian ports feature amongst the top 20 ports of the world. Interestingly, several ports in Asia, namely, seven ports from China, Singapore, Hong Kong and Malaysia are on that list. Several studies and reports have shown that privatization in India did not yield the desired results. Ports in India have adopted a hybrid mode of governance, aligned between a landlord port model and a service port model. This paper aims to address the question - What is the optimal way to mix privatisation and government control in the operations of major ports of India. Design/methodology/approach In this paper, the authors attempt to develop an optimization model for port planners to decide on the optimum mix of privatized and self-managed operations so as to maintain efficiency and maximize revenue. Findings The model tested on a major port in the country shows that the present privatization policy followed by the port needs revision. A similar plan to revise their policies can be carried out for other major ports in the country. Originality/value The model is generic and can be used by any port in the world operating under conditions similar to those in India.

关键词： Optimization Customer Privatization Cost-revenue dynamics Indian major ports mixed integer linear programming problem

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Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout

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COMPUTERS & CHEMICAL ENGINEERING 2018年 108卷 163-178页

作者： Rosa, Vinicius Ramos Camponogara, Eduardo Martins Ferreira Filho, Virgilio Jose Petr Brasileiro SA Rio De Janeiro Brazil Univ Fed Santa Catarina Dept Automat & Syst Engn Florianopolis SC Brazil Univ Fed Rio de Janeiro Dept Ind Engn Rio De Janeiro Brazil

This paper presents a practical and effective optimization method to design subsea production networks, which accounts for the number of manifolds and platforms, their location, well assignment to these gathering systems, and pipeline diameter. It brings a fast solution that can be easily implemented as a tool for layout design optimization and simulation-based analysis. The proposed model comprises reservoir dynamics and multiphase flow, relying on multidimensional piecewise linearization to formulate the layout design problem as a MILP. Besides design validation, reservoir simulation serves the purpose of defining boundaries for optimization variables and parameters that characterize pressure decrease, reservoir dynamics and well production over time. Pressure drop in pipelines are modeled by piecewise-linear functions that approximate multiphase flow simulators. The resulting optimization model and approximation methodology were applied to a real oilfield with the aim of assessing their effectiveness. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Subsea infrastructure Manifold placement Pipeline design Piecewise-linear approximation mixed integer linear programming problem

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Framework of intentional islanding operation of active distribution network based on self-healing perspective

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IET RENEWABLE POWER GENERATION 2018年第2期12卷 219-226页

作者： Oboudi, Mohammad Hossein Hooshmand, Rahmat-Allah Faramarzi, Farshad Boushehri, Mohammad Javad Amroony Univ Isfahan Dept Elect Engn Esfahan Iran

Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a new two-stage methodology is proposed to supply critical loads based on cost-effective improvement. In the first stage, the interruption cost is proposed as the load priority and the ON/OFF status of switches are considered as the binary decision variables. Therefore, IIO is considered as a mixed integer linear programming (MILP) problem to minimise the interruption cost. At the second stage, the power flow calculation is performed on the initial islands for the real-time operation. The proposed method can be utilised for both long- and short-term studies. In the long-term study, the inherent uncertainty of ADN is considered in MILP by using a Monte-Carlo simulation. This concept is used for clustering ADN into self-sufficient microgrids. Moreover, by taking a snapshot of the ADN status and performing operational feasibility, the proposed method can be considered as a real-time power regulation method. The proposed methodology is implemented on the IEEE 33-bus distribution network, and the results are discussed in detail.

关键词： power distribution reliability distributed power generation integer programming linear programming costing power distribution economics Monte Carlo methods intentional islanding operation active distribution network self-healing perspective ADN reliability local DG two-stage methodology interruption cost load priority switch on-off status binary decision variables mixed integer linear programming problem MILP problem power flow calculation inherent uncertainty Monte-Carlo simulation ADN clustering self-sufficient microgrids real-time power regulation method IEEE 33-bus distribution network

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