Optimal Capacity Allocation Research Articles

Research on optimal allocation of energy storage capacity of microgrid considering various factors

For the grid connected by high proportion distributed power, energy storage system can effectively reduce the adverse impact of micro grid power fluctuation on the grid. Because of the high cost of energy storage system, it is necessary to optimize the capacity of energy storage system. Therefore, this paper proposes a capacity optimization method of energy storage system. Based on the comprehensive consideration of system objectives, construction costs, energy storage security and other factors, the simulation analysis model of the system is established, and the control strategy is optimized. In this paper, the algorithm is applied to the construction of microgrid demonstration project. The experimental results verify the effectiveness of the proposed method.

Optimizing capacity allocation in semiconductor manufacturing photolithography area – Case study: Robert Bosch

In this paper, we advance the state of the art for capacity allocation and scheduling models in a semiconductor manufacturing front-end fab (SMFF). In SMFF, a photolithography process is typically considered as a bottleneck resource. Since SMFF operational planning is highly complex (re-entrant flows, high number of jobs, etc.), there is only limited research on assignment and scheduling models and their effectiveness in a photolitography toolset. We address this gap by: (1) proposing a new mixed integer linear programming (MILP) model for capacity allocation problem in a photolithography area (CAPPA) with maximum machine loads minimized, subject to machine process capability, machine dedication and maximum reticles sharing constraints, (2) solving the model using CPLEX and proofing its complexity, and (3) presenting an improved genetic algorithm (GA) named improved reference group GA (IRGGA) biased to solve CAPPA efficiently by improving the generation of the initial population. We further provide different experiments using real data sets extracted from a Bosch fab in Germany to analyze both proposed algorithm efficiency and solution sensitivity against changes in different conditional parameters.

Zonotope‐based method for optimal allocation of wind capacity in microgrids considering generation uncertainty

The increasing wind capacity integration brings new challenges to power system planning and operation, due to the intermittency and uncertainty of its power generation. This study determines the optimal allocation of wind capacity in microgrids, with considerations of the impact of generation uncertainty on both static and dynamic performance of the system, for reducing the possible violations of operation limits caused by wind power fluctuations. The system performance under uncertainty is evaluated using a zonotope-based set-theoretic method. The uncertain variations of wind power are modelled by a zonotope, then the uncertainty sets of static and dynamic state variables, which capture all possible values of the system states, are further calculated by set operations and are embedded in the optimisation model as constraints for system performance. The proposed method is applied to a 33-bus microgrid; the results prove the validity of the method and show that the system can be guaranteed to operate within acceptable security regions in all possible situations when subjected to certain level of uncertainty.

Optimal capacity allocation under random passenger demands in the high-speed rail network

The capacity allocation is a practically significant factor to influence the quality of the train timetables in the rail operations, especially under the fluctuation of passenger demands. This paper aims to investigate a detailed description for the structure and characteristics of the capacity allocation problem under random demand in the high-speed rail network. A two-stage stochastic integer programming model is provided to get the capacity allocation solutions to meet random fluctuations of passenger demands in the daily operations, which incorporates demand uncertainty and makes no assumptions for the rail network structure and distribution of passenger demands. Given the inherent complexity for solving this problem, we provide a solution framework including a heuristic algorithm based on tabu search in order to obtain a near-optimal solution and strategies to obtain an efficient timetable and train formation adjustment. Finally, two sets of examples, in which a sample rail network with 5 stations and the Beijing-Shanghai high-speed rail network data are adopted as the experimental environments to illustrate the performance and effectiveness of the proposed methods.

Optimal Allocation of Power Capacity of Island Microgrid With Long-Term Grid-Connected Operation

Optimal Allocation of Power Capacity of Island Microgrid With Long-Term Grid-Connected Operation

Electric vehicle battery capacity allocation and recycling with downstream competition

We study the optimal channel choice and battery capacity allocation strategies of an electric vehicle (EV) manufacturer in the presence of battery recycling. The EV manufacturer also produces EV batteries. As an upstream manufacturer, this firm has the option to supply batteries to its competitor, a downstream EV manufacturer which is unable to produce batteries. We characterise the equilibrium decisions of the two EV manufacturers under different channel structures. One finding is that the optimal channel choice of each EV manufacturer depends on the procurement cost from an external battery supplier. If the procurement cost is moderate, the downstream manufacturer prefers to order from the upstream manufacturer and the upstream manufacturer will supply batteries to satisfy the order. In addition, we investigate the effects of key parameters on the equilibrium capacity allocation decisions and manufacturers’ profits using numerical experiments. We also show that to maximise social welfare, the upstream EV manufacturer should not supply batteries to its competitor if the procurement cost from the external supplier is low, which is contrary to the case of profit maximisation.

Capacity Allocation with Multiple Suppliers and Multiple Demand Classes

We consider the capacity allocation problem for a retailer with multiple suppliers and multiple demand classes. The retailer offers one seasonal product and reserves capacity from multiple suppliers. Customers in different classes are charged with different selling prices for the same product. We analyze the optimal capacity allocation policies with the following three types of customers: (i) patient customers, (ii) impatient customers, and (iii) customers with limited patience. To empower our analysis, we propose a new preservation property of decomposition under a maximization operator. Based on the preservation property, we show that the value function in each period is decomposable for each type of customers. We then characterize the optimal capacity allocation policy for each type of customers and develop an efficient algorithm to obtain the respective optimal policy by exploiting decomposition. We also numerically investigate the optimal policy and show its value against a counterpart static heuristic policy. Finally, we extend our results to systems with multiple products, new capacity additions, etc.

Capacity planning and optimization of business park-level integrated energy system based on investment constraints

Through the coordination and complementarity of multiple energy sources, the optimal capacity planning of integrated energy system under limited financial constraints can promote the local absorption of renewable energy, realize the optimal utilization of resources and improve the utilization rate of comprehensive energy. Aiming at the integrated energy system formed by multi-energy coupling, this paper adopts three investment restraint schemes, simulates the economic operation of the system based on typical daily load characteristic curves in different seasons, and establishes an optimal capacity allocation model of the integrated energy system taking into account the investment cost restraint and minimizing the total annual cost and carbon dioxide emissions. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) are used to optimize the solution. Finally, a park in Beijing is taken as an example to verify the model optimization results and the actual results. The deviation of the target results is less than 5%. This study realizes the scientific capacity allocation of integrated energy system, and provides theoretical basis and technical support for the planning and design of integrated energy system.

Optimal Capacity Design of Independent Micro-Grid System for High Proportion of Wind-Solar Combined Cooling Heating and Power System with Energy Storage

This paper researches the optimal capacity design of independent micro-grid system for wind-solar combined cooling heating and power system with energy storage, puts forward a dispatching method based on expert time-judge for dynamic control of wind-solar direct permeability and a hierarchical design based on energy storage module. Because of the uncertainty of wind-solar system, this paper adopts fuzzy parameters to describe the system constraints and clarify the opportunity constraints, then optimizes the capacity design on fuzzy constraints and particle swarm optimization, analyses the operation results of optimal capacity allocation under dispatching method and hierarchical design of energy storage.

Capacity-Control Optimization of SMES in Distribution Networks With Renewable Energy

The demand for electric power has grown rapidly due to social and economic developments. The limited capacity of distribution network lines in many remote areas has led power consumers to suffer greatly from voltage drops at the user terminal, which then lowers the voltage at the distribution network. An effective way to address the imbalance between supply and demand in these areas involves applying renewable-energy-distributed generation. Yet because distributed generation can be random and intermittent, this intervention will bring new problems and challenges to the grid. Energy storage devices have been widely applied to solve these problems. Among such devices, superconducting magnetic energy storage (SMES) has characteristics of a long recycle life and high conversion rate, making it suitable for distributed generation. In this paper, for a distribution network with distributed generation, SMES is used to improve the voltage stability of distribution network nodes and provide economical access to the interconnection of renewable energy. A joint scheduling method of SMES optimal capacity allocation and energy management is proposed in this paper.

An Innovative Planning Method for the Optimal Capacity Allocation of a Hybrid Wind–PV–Pumped Storage Power System

In recent years, wind and photovoltaic power (PV) have been the renewable energy sources (RESs) with the greatest growth, and both are commonly recognized as the major driving forces of energy system revolution. However, they are characterized by intermittency, volatility and randomness. Therefore, their stable and efficient implementation is one of the most significant topics in the field of renewable energy research. In order to improve the stability of RESs and reduce the curtailment of wind and solar energy, this paper proposes an innovative planning method for optimal capacity allocation. On one hand, a new power generation system is introduced which combines a pumped storage power station with a wind farm and PV; on the other hand, the sequential Monte Carlo method is utilized to analyze the economy and reliability of the system under different capacity configurations considering investment cost, operating characteristics and influence factors of wind and solar energy. Then, optimal capacity allocation can be achieved. In summary, this proposed scheme provides an effective solution for the planning and construction of a new power generation system with RESs.

Optimal allocation of wind capacity considering the impact of generation uncertainty based on zonotope limited security regions

The increasing wind capacity integration brings new challenges to power system planning and operation, due to the intermittency and uncertainty of its power generations. This paper determines the optimal allocation of wind capacity with considerations of the impact of uncertainty in wind generation using the zonotope-based method, for the sack of reducing the possible violations of operation limits caused by wind power fluctuations. The uncertain variations of wind power are bounded by a zonotope and then propagated through a linearised power flow to get another zonotope, which captures all possible values of the system state variables. The state-bounding zonotope is then added to the optimal model to quantify and assess the impact of generation uncertainty on system static performance. The proposed method is applied to IEEE 33-bus system, the results prove the validity of the method and show that the zonotope based set-theoretic method is more efficient than probabilistic methods when focusing on the operation limits with considerations of the uncertainty of multiple renewable energy sources.

Scheduling Elective Surgeries with Emergency Patients at Shared Operating Rooms

Operating rooms (ORs) are the greatest source of revenues for hospitals and also their largest cost centers. When scheduling surgeries, hospitals face a trade‐off between the need to conduct planned elective surgeries and the need to be responsive to emergency cases. However, scheduling ORs, especially at Level‐1 trauma hospitals, is challenging due to significant uncertainties in the arrivals of patients requiring emergent surgery. The issue of allocating limited capacity to emergent surgery cases while scheduling elective patients has major policy implications. We develop a model for allocating the OR capacity to elective patients in such a way that the emergency patients who arrive randomly can be accommodated without incurring excessive delays. The objective is to develop a framework for aggregate weekly schedules and generate detailed daily schedules that minimize the total cost of the ORs’ expected operating time, idle time, and overtime. We present optimization procedures for generating effective schedules and rescheduling procedures that adjust the schedules of elective patients affected by emergency arrivals. Initially, the procedures assume deterministic surgery times for elective patients; the procedures are then extended to include stochastic surgery times. We show that for a given arrival rate of emergency patients, the total expected cost is convex in the weekly load of elective surgeries being scheduled. Numerical experiments are devised to obtain total expected cost curves for various emergency arrival rates. Using these curves, the optimal capacity allocation of ORs to elective patients can be determined as a function of the emergency arrival rate.

Collaborative optimisation of resource capacity allocation and fare rate for high-speed railway passenger transport

The reasonable pricing of high-speed railway tickets and the optimal allocation of transport resource capacity can not only enhance competitiveness in the transport market, but also reasonably coordinate the revenue of the enterprise and utilities to passengers. This study uses price signals to adjust resource capacity allocation; and develops a co-optimisation model of resource capacity allocation and fare rates of high-speed trains in different train operation routes. The developed model aims at the comprehensive optimisation of railway enterprise's revenue and passengers' travel benefits, with the ratio of supply-demand and the floating rate of the fare as the main constraints. The Particle Swarm Optimisation (PSO) algorithm is applied to obtain the seat resource allocation scheme and the optimal fare rate for each train operation route. Finally, the case analysis is carried out to test the model and the algorithm. Based on a statistical analysis of actual ticket sale data of the Beijing-Shanghai high-speed railway for a certain month, the optimal unit fare and optimal seat resource allocation scheme are obtained to meet the corresponding passenger demand. The case analysis shows that after optimisation by the proposed method, the total value of the objective function is 2.04% higher than that before optimisation.

Optimal capacity allocation for high occupancy vehicle (HOV) lane in morning commute

In this paper, we analyze the optimal capacity allocation for high occupancy vehicle (HOV) lane in morning commute, using the classical bottleneck model to a single corridor. The vehicular traffics are divided as high occupancy vehicle (HOV) and low occupancy vehicle (LOV) based on the number of in-vehicle travelers. The whole corridor is allocated as general purpose (GP) lane and HOV lane, separately with a bottleneck at the end of each lane. The optimal capacity of HOV lane for minimizing the system’s total cost is analytically derived. It is found that with optimal capacity of HOV lane, each HOV traveler experiences lower cost than each LOV traveler. Numerical examples are presented to verify all analytical results.

Optimal Dynamic Capacity Allocation for High Throughput Satellite Communications Systems

In this letter, a simple engineering scheme for achieving optimal dynamic capacity allocation in geostationary and medium Earth orbit multi-beam high throughput satellite systems is presented. Exploiting smart gateway diversity setup and considering users’ requested and gateways’ offered capacities, we propose a theoretically optimal, based on Monge arrays, capacity allocation scheme for minimization of both system’s capacity losses and rate matching performance metrics. This scheme has low complexity considering the appropriate sorting of system’s offered and requested capacities. Finally, simulation results also confirm its optimality by presenting its identical performance with the much more complicated and time consuming exhaustive mechanism.

Optimal Capacity Allocation when Patients encounter Congestion in Primary Healthcare Network

Universal health care aims at providing low cost or if possible free primary care to everyone. Most countries pursue this goal and it is pertinent for developing countries to make the best use of their limited resources to achieve it. In spite of every effort from the government, unfortunately patients in India spend significant amount of money on travelling and out-of-pocket expenses for availing primary care services even at public funded facilities. We propose an optimization model to help health decision makers in managing existing capacity for alleviation of this problem. The model can be used in the identification of existing health care facilities that need to be upgraded or reduced with a view to improve their utilization at minimum cost. The model recognizes increase in patient out-of-pocket expenses incurred at facilities due to longer waiting time (congestion). Results from numerical experiments are presented to explicate the functioning of the model.

A hierarchical location model for determining capacities of neonatal intensive care units in Korea

Reforming the healthcare delivery system to provide optimum care to sick newborn infants is a critical task in Korea. Motivated by the efforts of the Korean government, we study a capacity allocation model to design an optimal capacity allocation plan for neonatal intensive care units (NICUs). Our model considers the following properties: 1) the hierarchical feature of neonatal care services and 2) the congestion effect in NICU operations. We develop a mathematical model that combines a hierarchical location model with queuing theory. We subsequently apply the proposed model to the problem of allocating capacities to NICUs in Korea. We provide information that can help policymakers draw an initial plan by evaluating various capacity allocation scenarios. We further examine two policy alternatives for improving accessibility to neonatal care. One involves increasing service capacity by adaptively adding resources to NICUs, and the other includes expanding physical service coverage by introducing helicopter transport. The results show that each alternative can contribute toward improving accessibility, and we believe that these findings will have practical implications for developing a better neonatal care system.

Research on capacity optimization configuration of AC/DC hybrid microgrid interconnect converter

Interconnected converters are an important part of AC/DC hybrid microgrids. This paper fully considers various factors affecting the capacity configuration of interconnected converters, and proposes the principle of optimal capacity allocation to meet the power exchange requirements between AC and DC hybrid microgrids and meet the requirements of reliable power supply for important loads. According to the operation requirements of AC/DC hybrid microgrid under different scenarios, two optimal capacity allocation methods for power supply continuity and economy are given. Finally, the AC/DC hybrid microgrid project with island operation is taken as an example to verify the effectiveness of the proposed method, and the influence of important parameters is discussed in depth. The optimal configuration of interconnected converter capacity is given.

A Stochastic Queueing Model for Capacity Allocation in the Hierarchical Healthcare Delivery System

We use the capacity allocation as a demand management tool to optimize the patient flow distribution on a hierarchical healthcare delivery system, which is a mixture of patient choice and gatekeeping. Capacity allocation for such service system can be challenging because of the inherent stochastic referral process and patients’ heterogeneous delay sensitivities. In this research, a stochastic queueing-based model is proposed to find the optimal allocation of the limited service capacity of the second level of experts. Considering the impact of the deficiency of the skill level and the amount of gatekeepers, the stochastic referral process is modeled with a tandem queue. By solving a fixed-point problem, we show that there is an unique optimal allocation and corresponding equilibrium demand. We carry out numerical studies and find that providing two alternatives for patients can be better than gatekeeper system, when the capacity of the gatekeeper is moderate compared to patients’ potential demand. Results also indicate that the optimal allocation is robust in terms of the referral rate and the mistreatment rate when two rates are less than corresponding thresholds.