Optimization Model For Planning Research Articles

Bi-level distributionally robust optimization model for low-carbon planning of integrated electricity and heat systems

Interdependence among diverse energy forms within integrated electricity and heat systems (IEHSs) holds the potential to harness cooperation effects. This paper proposes a bi-level optimization model for the low-carbon planning of IEHSs, which realizes the coordination between IEHSs and their upstream networks. The precise carbon emissions of IEHSs are traced through carbon emission flows of components, power distribution networks, and district heating networks. Moreover, the distributionally robust optimization approach is employed to account for the inherent uncertainty in renewable power outputs faced by IEHS planning, by incorporating the moment information into an ambiguity set. The bi-level distributionally robust planning model of IEHSs is initially reconfigured into a second-order cone structure, employing the strong duality theory, second-order cone duality theory, and linear decision rules. Subsequently, an iterative approach is introduced to attain convergence of the reformulated bi-level model. Numerical results demonstrate the effectiveness and superiority of the proposed approach for reducing carbon emissions and economic costs of IEHSs.

Optimization method for renewable energy investment planning in the electricity carbon coupling market environment considering economy

China is developing rapidly in renewable energy development, electricity market reform, and carbon market construction, using market mechanisms for efficient planning and investment. The electricity and carbon markets are interrelated for market entities. This context has led to a dual-level optimization model for renewable energy investment planning, considering cost recovery and new power system goals. The model includes deriving electricity carbon coupling quotations for conventional units based on dynamic carbon emission characteristics and proposing an electricity spot market mechanism influenced by carbon quota trading. The model’s upper level maximizes renewable energy investment capacity, determining its size and location, while the lower level simulates electricity spot and voluntary emission reduction market trading to guide investment decisions. The model transformed into a mixed integer linear programming problem for optimal solutions was validated numerically, examining various factors affecting optimal renewable energy investment capacity.

Intelligent mission planning of sky survey based on the NSGA-III algorithm

In recent years, China has been scheduled to launch an all-sky survey telescope for celestial observation. The meticulous arrangement of observation sequences for this telescope is of paramount importance, given the myriad challenges and constraints inherent to such astronomical survey missions. These challenges encompass factors such as spatial limitations, stray light contamination, as well as constraints related to data storage and energy consumption. This undertaking presents a quintessential NP-hard optimization problem. To address this complexity and enhance in-orbit observation efficiency, this study closely adheres to practical engineering considerations. A comprehensive evaluation of diverse constraints is conducted, culminating in the establishment of an optimization model for systematic planning of sky survey missions. The primary optimization objectives encompass maximizing in-orbit observation duration, augmenting the count of observable celestial points, and minimizing maneuvering angles. To tackle this multifaceted problem, a mission planning algorithm is devised based on NSGA-III, a multi-objective optimization technique. The experimental results indicate that the sky survey planning algorithm proposed in this paper can effectively accomplish sky survey observation tasks, achieving the scientific objective of no fewer than 350 daily observation points. It holds the potential to provide algorithmic support for the sky survey telescope mission on the Chinese space station.

Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

This paper presents a maintenance model for optimal planning of pipeline network inspections subjected to corrosion and residual stress. The proposed model includes predictive degradation modeling, spatio-temporal reliability, and expected cost minimization. The pipeline networks are formed by linear segments and irregular zones such as elbows, weld joints, and flanges for putting pipes together and changing the direction of gas or liquid flow. The mean of Karhunen-Loève expansion is used to model space-variant corrosion, residual stress, and stochastic dependency in different exposure zones. Monte Carlo simulations are employed to compute the failure probability of individual components. Subsequently, a Bayesian network evaluates the failure probability of the complex pipeline network and identifies the most critical components. Depending on the objective or market demand uncertainties, a cost model is also proposed to optimize the inspection policy. The methodology is applied to a case study, and the results show that it can provide valuable insights to decision-makers to enhance complex systems' safety and reliability.

Research on end-route planning for community group purchasing for vehicles with different energy sources

To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development.

Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions

AbstractWith the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems.

A single-objective optimization model for power systems with the introduction of line-heavy load rate index constraints

The increasing penetration of new energy sources in new power systems has exacerbated the volatility and complexity of power system currents. Grid planning is facing higher challenges. Established grid planning models usually use the upper and lower limits of grid transmission capacity as constraints, but this may lead to long-term heavy load operation of certain lines and increase system risks. Therefore, this paper designs a line-heavy load rate constraint and establishes a single-objective optimization model for grid planning based on this constraint. Example analyses verify the effectiveness of adding line-heavy load rate constraints in planning and the reasonableness of the single-objective optimization model.

Bi-Layer Planning of Integrated Energy System by Incorporating Power-to-Gas and Ground Source Heat Pump for Curtailed Wind Power and Economic Cost Reduction

The popularization of renewable energy is limited by wasteful problems such as curtailed wind power and high economic costs. To tackle these problems, we propose a bi-layer optimal planning model with the integration of power to gas and a ground source heat pump for the existing integrated energy system. Firstly, the inner layer optimizes the daily dispatch of the system, with the minimum daily operation cost including the penalty cost of curtailed wind power. Then, the enumeration method of outer-layer optimization determines the device capacity of various schemes. After that, optimal planning can be achieved with the minimum daily comprehensive cost. The result of this example shows that the improved system can reduce curtailed wind power and system costs, thus improving the overall economy. Finally, the influences of algorithms and gas prices on planning optimization are studied.

Coordinated reactive power planning in an industrial park integrated energy system using support vector regression

In this study, we develop and present a comprehensive multi-objective optimization model for reactive power planning in industrial park integrated energy systems (IPIES), addressing the challenges posed by extensive inductive loads. Our model prioritizes enhancing system resilience through three key objectives: minimizing total costs, improving static voltage stability margins, and enhancing voltage recovery capabilities. We employ support vector regression (SVR) to estimate voltage recovery metrics effectively, using reactive power capacity as input. The optimization process leverages the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for efficient solution finding. Validation on the IEEE-39 bus system demonstrates the model’s effectiveness, with SVR significantly reducing computational demands while maintaining satisfactory accuracy.

An Improved Particle Swarm Optimization Algorithm for Automated Test Resource Allocation

With the rapid increase of software complexity, automated testing has become the mainstream of software testing. However, the current automated test tools adopt a large number of common test equipment resource. At the beginning of the test, for specific test scenarios, the testers need to plan the test equipment resources according to the requirements of the software under test. An automatic test equipment resource planning and optimization algorithm based on group optimization is proposed IPSO (Improved Particle Swarm Optimization) to solve this problem. This method maps the requirement description to the equipment description to obtain the matching matrix, and then establishes a multi-objective constrained optimization model for test equipment resource planning. The solution is obtained through improved particle swarm optimization algorithm to achieve the optimal planning of test equipment resources. Compared with traditional manual planning methods, this algorithm reduces the workload of testers, and improves the automation and efficiency of testing. Experimental results demonstrate that the optimal solutions found by the algorithm proposed in this paper is superior to those of the PSO (Particle Swarm Optimization)) and Linearly Decreasing Inertia Weight Particle Swarm Optimization (LWPSO) algorithms.

Co-optimization of system configurations and energy scheduling of multiple community integrated energy systems to improve photovoltaic self-consumption

Given the increasing integration of building photovoltaics and the accelerated transformation of power systems, the supply-demand imbalance in photovoltaic communities has become a pressing issue. This study presents a novel optimization model for collaborative planning and scheduling. The alternating direction multiplier method is utilized to derive an optimal economic scheduling strategy for the community cooperative alliance, while ensuring private information protection. Additionally, the genetic algorithms are employed to ascertain the optimal configuration. The optimization model prioritizes the common good over individual interests in community cooperation alliances, meaning some members may need to compromise their self-interest to attain optimized overall benefits. Therefore, a compensation mechanism based on the Nash negotiation principle is established to ensure fair distribution of benefits. The simulation results demonstrate that the primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of the photovoltaic community alliance are 42.91%, 32.50%, and 41.81%, respectively, and the self-consumption level of photovoltaic is vastly improved to 98.83% on a typical winter day. The sensitivity analysis illustrates that the photovoltaic panels in buildings contribute to a decrease in energy consumption and carbon emission by 25.57% and 26.15%, respectively. In contrast, they result in a cost increase of 11.05%.

Variable Time-Step Physics Engine with Continuous Compliance Contact Model for Optimal Robotic Grinding Trajectory Planning.

In the transition from virtual environments to real-world applications, the role of physics engines is crucial for accurately emulating and representing systems. To address the prevalent issue of inaccurate simulations, this paper introduces a novel physics engine uniquely designed with a compliant contact model designed for robotic grinding. It features continuous and variable time-step simulations, emphasizing accurate contact force calculations during object collision. Firstly, the engine derives dynamic equations considering spring stiffness, damping coefficients, coefficients of restitution, and external forces. This facilitates the effective determination of dynamic parameters such as contact force, acceleration, velocity, and position throughout penetration processes continuously. Secondly, the approach utilizes effective inertia in developing the contact model, which is designed for multi-jointed robots through pose transformation. The proposed physics engine effectively captures energy conversion in scenarios with convex contact surface shapes through the application of spring dampers during collisions. Finally, the reliability of the contact solver in the simulation was verified through bouncing ball experiments and robotic grinding experiments under different coefficients of restitution. These experiments effectively recorded the continuous variations in parameters, such as contact force, verifying the integral stability of the system. In summary, this article advances physics engine technology beyond current geometrically constrained contact solutions, enhancing the accuracy of simulations and modeling in virtual environments. This is particularly significant in scenarios wherein there are constant changes in the outside world, such as robotic grinding tasks.

A novel optimization method of carbon reduction strategies implementation for industrial parks

The effects of various energy conservation and carbon reduction (ECCR) strategies can differ significantly despite equal investment. Given limited amount of capital expenditure, managers and planners of industrial parks must carefully select from different ECCR strategies and implementation technologies to maximize investment returns. This study establishes mathematical models for four ECCR strategies: forestry carbon sequestration (FCS), carbon capture and utilization (CCU), waste heat recovery (WHR), and photovoltaic (PV). A universal ECCR planning optimization model is constructed to maximize annual economic benefits or carbon emission reduction. Using an industrial park in southern China as a case study, genetic algorithms are utilized to solve the model and validate its feasibility. The study analyzes three key parameters: capital expenditure caps, carbon trading price in the Emission Trading Scheme, and transportation distance of captured CO2 products for sensitivity. The results demonstrate considerable economic benefits of the CCU strategy when demand matches appropriately. However, in cases with limited capital expenditure, implementing small-scale FCS strategies in industrial parks is not advisable from both an economic and environmental perspective.

A risk-averse stochastic optimization model for community resilience planning

Community resilience planning is challenging as it involves several large-scale systems with interdependency, populations with diverse socio-economic characteristics, and numerous stakeholders. This study introduces a new optimization model to decrease a community's burden in developing viable alternative sets of decisions while considering costs and risks associated with uncertain hazard events. The model captures the essential features of a community, and its scope extends beyond infrastructure and buildings to include social goals. Structural engineering and social science approaches are adapted and incorporated into the model formulation to facilitate the identification of engineering decisions meeting the social goals of minimizing population dislocation and time for recovery. A risk-averse approach frames the optimization problem as a two-stage mean-risk stochastic programming model, which enables effective planning for low-probability, high-consequence hazard events. A case study simulating flood hazards in Lumberton, North Carolina, is developed, and the model is run with the generated data set to showcase the model's capability in developing risk-informed mitigation and recovery plans to achieve resilience goals. The insights drawn from the numerical experiments show the effect of changing risk preference on community resilience metrics.

Towards carbon neutrality: A multi-objective optimization model for photovoltaics systems installation planning

Increasing photovoltaic (PV) installed capacity is considered a primary way to achieve carbon neutrality targets. However, the rapid development has led to the PV market saturation in some areas, and solar curtailment has even occurred due to the oversupply. With more demand for PV installation in the future, the question of where to efficiently deploy PV systems to both meet the increasing PV electricity demand and alleviate the mismatch between PV supply and local demand becomes relevant. To explore these issues, this paper proposes an integrated framework for planning the PV installed capacity allocation. The framework can be decomposed into two stages: at the optimization stage, a multi-objective optimization (MOO) model is proposed, integrating the minimization of mismatch between PV supply and local demand as an additional objective along with the traditional economic and environmental profits maximization objective; and at the evaluation stage, an entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is employed to make the decision of the PV allocation scheme. By applying this integrated model to the Chinese PV deployment, the results reveal that the consideration of the match between PV supply and local demand has a direct influence on the PV installation layout. More than 50% of the PV systems are expected to be in areas with higher demand and higher profits, rather than the areas with high solar irradiance but lower demand. Distributed PV installations are expected to account for 60% of the total installed capacity. The implementation of this PV installation planning is estimated to result in a reduction of 1036 Mt CO2 emission, a 14.2% PV penetration rate, and 566 billion CNY total profits, making it an attractive and feasible instrument for countries striving towards carbon neutrality goals.

Optimization models for the installation planning of offshore wind farms

Challenges posed by global warming motivate the increasing interest in green energy sources, such as wind energy. To make wind energy attractive from an economic viewpoint, the decision-making problems faced in designing, installing, and maintaining wind farms have to be optimized. In this paper, we focus on the problem of optimally planning the installation process of offshore wind farms, as faced by Vattenfall, a leading European energy company. We first formulate the deterministic version of this Installation Planning of Offshore Wind Farms (IPOWF) problem as a Mixed Integer Linear Programming (MILP) model. From this model, we then derive other MILPs that can provide lower and upper bounds to the optimal installation cost. To cope with weather uncertainty, we also provide a light-robust MILP. These models are tested on real-life instances corresponding to two wind farms that Vattenfall has recently built in Denmark and Germany. Computational results show that tight primal and dual solutions can be computed for the deterministic case with the proposed models and that the light-robust model yields solutions that are robust to weather uncertainty.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

An Energy Storage Capacity Configuration Method for a Provincial Power System Considering Flexible Adjustment of the Tie-Line

A high proportion of renewable generators are widely integrated into the power system. Due to the output uncertainty of renewable energy, the demand for flexible resources is greatly increased in order to meet the real-time balance of the system. But the investment cost of flexible resources, such as energy storage equipment, is still high. It is necessary to propose a method for determining the capacity of energy storage scientifically. An optimization and planning method of energy storage capacity is proposed. It is characterized by determining the optimal capacity of energy storage by carrying out 8760 hours of time series simulation for a provincial power grid with energy storage. Firstly, the current situation of power supply and demand for provincial power grids is analyzed. The difficulty of realizing a power balance at different time scales is analyzed. Then, the source load balancing solutions at different time scales are proposed. The difficulty of a long-term power balance can be alleviated by flexibly adjusting the power on the tie-line of the provincial power grid. And the difficulty of a short-term power balance can be met through energy storage. In addition, an optimal planning model of an energy storage system is established with the power supply cost as the objective function. The optimal capacity of the energy storage is determined by comparing the objective function of different planning schemes. Finally, a case study is carried out. It is found that flexible adjustment of interprovincial interconnection lines can reduce the maximum demand for electricity from 8.439 billion kWh to 2.299 billion kWh. At the same time, the curtailment ratio of renewable electricity can be decreased from 12.6% to 5.0% by using energy storage. However, the average power supply cost of the system gradually increases from 0.307 CNY/kWh to 0.485 CNY/kWh. It is necessary to fully tap into the various values of energy storage equipment.

Optimization Models for Long-Term Planning of Municipal Solid Waste Management Systems: A Review with An Emphasis on Mass Balances

The vast majority of decision-making approaches used for long-term planning of municipal solid waste management systems (LPMSWMS) are ground on scenario-based structures. However, the scenario-based structures may overlook many real-world possibilities because of their restricted mass balances. This study is the first attempt to review the current state of optimization models, which are used as a decision-making approach for LPMSWMS, by focusing on the mass balances. In line with this purpose, 146 peer-reviewed articles were examined based on a new literature evaluation scheme. According to the findings, it can be stated that a significant majority of the articles offer non-deterministic optimization models dealing with the uncertain nature of the LPMSWMS problems. Considering all optimization models examined in the study, most of the model formulations have linear mathematical forms in terms of objective and constraint functions. However, it is quite interesting that none of the models produced solutions for a management system alternative with an integrated (non-restricted) mass balance. Accordingly, it is very questionable whether the results obtained from the current models have the power to give the most suitable solution for an up-to-date management system. As a result of the review, it is highly recommended that the optimization models to be conducted for the LPMSWMS in the future should search for new mathematical approaches considering the integrated mass balances under certainty and/or uncertainty.

A medical device distribution network design strategic planning process and optimisation model

A medical device distribution network design strategic planning process and optimisation model