Optimal Capacity Allocation Research Articles

Optimal Allocation of Energy Storage Capacity of High-Permeability Photovoltaic Power Generation System Based on Elastic Neural Network

The energy storage capacity configuration of high permeability photovoltaic power generation system is unreasonable and the cost is high. Taking the constant capacity of hybrid energy storage system (Hess) composed of high permeability wind frame and super capacitor as the standard, in order to ensure smooth and stable output of photovoltaic power equipment, this paper adopts the power suppression method based on empirical mode decomposition to decompose the original power into high and low frequency components, and uses super capacitor and energy storage battery to stabilize the high and low frequency components, Reduce output power fluctuation; Taking the minimum energy storage cost as the objective function, the optimal allocation of system energy storage capacity is realized by the recursive algorithm of elastic neural network. The experimental results show that when the supercapacitor capacity and photovoltaic electric field energy storage cell capacity are 0.262 mw· h and 2.889 mw· h respectively, the minimum cost is 16987 yuan, and the smoothness index is less than 5%; Compared with before the optimal configuration, the output power of the photovoltaic power generation system after the optimal configuration is smoother, the power peak valley difference is lower, and the loss can be reduced.

Optimal capacity allocation for heavy-traffic fixed-cycle traffic-light queues and intersections

Setting traffic light signals is a classical topic in traffic engineering, and important in heavy-traffic conditions when green times become scarce and longer queues are inevitably formed. For the fixed-cycle traffic-light queue, an elementary queueing model for one traffic light with cyclic signaling, we obtain heavy-traffic limits that capture the long-term queue behavior. We leverage the limit theorems to obtain sharp performance approximations for one queue in heavy traffic. We also consider optimization problems that aim for optimal division of green times among multiple conflicting traffic streams. We show that inserting heavy-traffic approximations leads to tractable optimization problems and close-to-optimal signal prescriptions.

Optimization Method of Combined Multi-Mode Bus Scheduling under Unbalanced Conditions

In view of the spatial and temporal imbalance of residents’ travel demands and challenges of optimal bus capacity allocation, in this paper the grand station express bus scheduling mode is introduced in the direction of heavy passenger flow during peak hours. Coordinated scheduling combining whole-journey and grand station express buses is adopted, and the station correlation calculation model is used to determine the optimal stops of the grand station express bus. Thus, a two-way bus scheduling optimization model for peak passenger flow is established with the goal of minimizing the total cost of passenger travel and enterprise operation. Finally, the nonlinear inertia weight dynamic cuckoo search algorithm is selected for the model’s solution, and the established scheduling optimization model is solved by combining basic data such as the study line’s bus Integrated Circuit (IC) card data. The effectiveness of the model is verified through a comparative study and evaluation of the solution.

Masking primal and dual models for data privacy in network revenue management

We study a collaborative revenue management problem where multiple decentralized parties agree to share some of their capacities. This collaboration is performed by constructing a large mathematical programming model that is available to all parties. The parties then use the solution of this model in their own capacity control systems. In this setting, however, the major concern for the parties is the privacy of their input data, along with their individual optimal solutions. We first reformulate a general linear programming model that can be used for a wide range of network revenue management problems. Then we address the data privacy concern of the reformulated model and propose an approach based on solving an equivalent data-private model constructed with input masking via random transformations. Our main result shows that, after solving the data-private model, each party can safely access only its own optimal capacity allocation decisions. We also discuss the security of the transformed problem in the considered multi-party setting. Simulation experiments are conducted to support our results and evaluate the computational efficiency of the proposed data-private model. Our work provides an analytical approach and insights on how to manage shared resources in a network problem while ensuring data privacy. Constructing and solving a collaborative network problem requires information exchange between parties that may not be possible in practice. Including data privacy in decentralized collaborative network revenue management problems with capacity sharing is new to the literature and relevant to practice.

Optimal outpatient capacity allocation under the capacity-addition policy

Outpatient departments are under great pressure to meet increasing demand with limited medical resources. Facing a high-demand environment, the capacity-addition policy (CAP) has been widely applied, where outpatients allocate additional capacity in a demand-driven approach. Under the CAP, outpatient managers estimate an effective demand. Additional capacity is determined depends on the relationship between the estimate and regular capacity. Moreover, regular capacity is also a decision variable, which leads to great difficulties when conducting capacity allocation. The joint decisions on the optimal regular capacity, its allocation scheme of different phases and proper rule for estimating the effective demand become important concerns for outpatient managers, under uncertainties in demand and patient no-show. This paper addresses the problem towards maximizing the expected profit composed of expected revenue minus the costs of idleness and overwork. Several properties are proposed to address the difficulty in the infinite solution space. Properties help transform the joint decisions into orderly single decisions and prove the unimodality of the objective. Algorithms are designed accordingly. Experiments indicate that it is better to pre-offset the effect of no-show rather than consider its reduction in the determination of additional capacity. Outpatient managers need to enlarge the estimate of effective demand, and 1.1 to 1.2 times of the total expected demand is suggested, where outpatients can balance the expected overload with the expected profit. The optimal regular capacity is stable to the variety of no-show probability.

Optimize train capacity allocation for the high-speed railway mixed transportation of passenger and freight

The collaborative transportation strategy of passengers and freights can improve the efficiency and revenue of the high-speed railway (HSR) system. This paper focuses on the train capacity allocation problem for the mixed transportation pattern of passenger and freight in HSR systems, in which rail operators introduce revenue management to determine the optimal train capacity allocation plan for each train service. We first propose a general train capacity allocation model which addresses passenger priority and freight loading/unloading capacity, and then the deterministic and stochastic demand scenarios are considered respectively. With the deterministic demand, the train capacity allocation model is linear programming with the objective of maximizing the revenue of the HSR system. While a non-linear programming model is built for the stochastic demand to maximize the expected revenue. For solving the problem with stochastic demand, the non-linear programming model is transformed into a mixed integer linear programming model, which can be easily solved by the existing solver to obtain the optimal solution. Two different-sized numerical experiments are conducted to demonstrate the efficiency and effectiveness of the proposed methods.

Optimal flow and capacity allocation in multiple joint quickest paths of directed networks

Shared vertices or edges in joint paths bring difficulties to flow routing and scheduling with delay requirements in networks with consideration of both edge lengths and capacities since flow along the different paths will encounter each other in capacitated edges with time dislocation. For an amount of flow, the quickest path problem (QPP) presents a good link for path lengths and capacities with the transmission time of the flow. Extended from the QPP, we propose an edge-path form traffic model for an amount of flow through multiple joint paths with different lengths in one-source one-sink directed capacitated networks. Then, an optimization model for minimum transmission time within feasible traffic constrained by edge capacities is constructed. We then derived the vertex–edge form of the optimization model from the edge-path form. The proposed optimization models in both forms are proved to be linear fractional programming problems, which can be solved in polynomial time. A routing algorithm based on the solution of the vertex–edge form optimization is developed combined with the DFS-based route-searching method. The proposed model and algorithm could be applicable in the real-time operation and management of practical network systems.

Optimal capacity allocation in accordance with renewable energy sources: the US electricity market

ABSTRACT Electricity generation cost and environmental effects of electricity generation continue to be among central themes in energy policy and planning. This study aims to forecast the optimal capacity expansion of the electric power sector in the US energy market for 2022–2050. We propose a fuzzy multi-objective linear programme to minimise cost and environmental effects. We depict how different policies and price evolution may alter the capacity composition by employing sensitivity analyses on contributing factors. The effects of the new composition and implied generation on the cost of electricity and emissions are examined, and yield that direct modelling of capacity factors is meaningful to be considered in energy strategy planning. In the literature, the optimal mix is usually found via a dispatch model yielding hourly or sub-hourly dispatch decisions, which is computationally demanding. As a contribution, we overcome this task with a simpler and effective approach, by modelling of capacity factors directly. Results show that renewable energy is expected to reach more than 1100 GW installed capacity in the US by 2050 by reducing the average cost of electricity generation and C O 2 emissions by more than 70% and 80%, respectively, compared to end-of-2021 estimates.

Optimal capacity allocation method of integrated energy system considering renewable energy uncertainty

With the reduction of fossil energy and the increase of energy consumption, the development and utilization of new energy is an inevitable trend. Renewable energy has attracted attention because of its cleanness and abundance. However, affected by the randomness and intermittence of renewable energy, it is difficult for the traditional power system to meet the needs of users after renewable energy is connected, and it is difficult to solve the consumption problem of renewable energy by relying on the traditional power system alone. To address this problem, an integrated energy system (IES) is constructed using a two-layer optimization method for the operation strategy and capacity allocation of the integrated energy system, and a particle swarm optimization algorithm is used to solve the multi-objective problem, taking renewable energy consumption, operation cost, and investment cost as the optimization indexes, and considering the equipment operation characteristics, uncertainty of renewable energy and the model constraints. The optimization results obtained from the solution are compared with the traditional energy supply system, and it is verified that the proposed method can achieve the lowest cost investment in the system while satisfying the reliability and safety constraints.

An Adaptable Correlated Control for Maintaining Voltage Quality in Low-Voltage Distribution Grids Containing PVs and PEVs

Rooftop photovoltaic (PV) systems and plug-in electric vehicles (PEVs) will be increasingly utilized by residential customers due to their economic and environmental benefits. However, the intermittent power produced by PVs and the additional loads required for the PEV charging can cause power quality disturbances in the low-voltage distribution networks (LVDN). An adaptable correlated control (ACC) is proposed for the decoupled optimization of the active and reactive power, where energy cost can be minimized through the local demand-side management (DSM) of residential loads. Further, the optimal allocation of the sparse capacity of inverters for reactive power compensation to maintain the voltage variations and the mitigation of voltage unbalances within the allowable limits is proposed. The proposed ACC provides a supervisory control to observe and modify the voltage quality of the LVDN without the need for real-time collaboration between controllers. The impacts of the integration of installed rooftop PVs and PEV charging for unbalanced three-phase LVDNs are assessed rigorously using a versatile simulation platform. Also, the suitability of the proposed control strategy for DSM and the alleviation of voltage fluctuations and unbalances are assessed.

Research on the optimal allocation method of source and storage capacity of integrated energy system considering integrated demand response

The optimal allocation of energy storage capacity is an important issue for integrated energy systems (IES). To reduce the impact of volatility and intermittency of renewable energy sources, the impact of volatility needs to be smoothed out by rational allocation of energy storage. To address the above issues, this paper proposes a method for the optimal allocation of source storage capacity considering integrated demand response(IDR). Firstly, the basic mechanism of IES based on energy hub(EH) is constructed, and the model and coupling relationship of equipment components in the system are introduced. The IDR model is built to optimize the load curve according to the real-time market tariff scheme, and then the confidence interval method is used to analyze the scenery uncertainty in IES and determine the scenery output curve under different confidence levels. On this basis, a bi-level optimization model is built that takes into account both source and storage capacity allocation and operational optimization. The upper-level planning model takes into account the uncertainty of wind power and photovoltaic output, and solves the allocation scheme of energy storage intending to minimize the total planning cost; the lower-level operation optimization model takes into account the output constraints of each unit and optimizes the output of the equipment to minimize the operation cost. Finally, it is verified that considering IDR can reasonably optimize the allocation of the system’s source storage capacity and reduce the system’s investment and operating costs.

A Singular Spectrum Analysis and Gaussian Process Regression-Based Prediction Method for Wind Power Frequency Regulation Potential

The development of primary frequency regulation (FR) technology has prompted wind power to provide support for active power control systems, and it is critical to accurately assess and predict the wind power FR potential. Therefore, a prediction model for wind power virtual inertia and primary FR potential is proposed. Firstly, the primary FR control mode is divided and the mapping relationship of operating wind speed and FR potential is constructed. Secondly, a hybrid prediction method of singular spectrum analysis (SSA) and Gaussian process regression (GPR) is proposed for predicting the speed of wind. Finally, the wind speed sequence is adopted to calculate the FR potential with various regulation modes in future time. The results show the advantages of the proposed method in the prediction accuracy of wind power FR potential and the ability to characterize the uncertainty information of the prediction results. Accurate modeling and prediction of wind power FR potential can significantly promote wind turbines to implement fine control of primary FR and optimal allocation of FR capacity within wind farm and group. Based on the actual operation data, the deterministic prediction and probability prediction of the FR potential of wind farms are conducted in this paper.

Capacity provisioning for evacuation on path networks

AbstractDesigning an appropriate evacuation plan in the event of large scale disasters is extremely important. A considerable research effort has been invested in effective mathematical models and algorithms for the evacuation of densely populated areas. Dynamic networks and flows, and the closely related sink location and evacuation problems are research directions that are being actively pursued at the moment. In this work, we propose an extension of the sink location problem in dynamic networks that is suitable for planning the evacuation of remote and sparsely populated communities. We exploit the connection between the capacity of a transportation network and the amount of resources allocated to the rescue operation, and we introduce a new objective function for the problem of evacuation in a dynamic network. Given a network with known edge lengths and with a known number of evacuees located at the vertices, we would like to assign capacities to the edges of the network, from a fixed capacity budget, in such a way that the evacuation time of all evacuees to the set of fixed sink nodes in the network is minimized. We consider a simple path network with one fixed sink node and observe that the solution can be obtained numerically from a non‐linear program. However, by exploiting two useful properties of the optimal capacity allocation, we propose a combinatorial algorithm that returns a parametric solution, that is, a solution that depends on a single parameter, namely the capacity assigned to one edge of the path. Our algorithm runs in time where is the budgeted capacity, is the precision to which the solution is computed, and is the number of vertices occupied by evacuees in an ‐vertex path. This work is the first to consider the capacity allocation problem in the context of sink location problems.

Research on capacity and strategy optimization of combined cooling, heating and power systems with solar photovoltaic and multiple energy storage

Combined cooling, heating and power systems can exploit renewable energy and significantly improve the comprehensive benefit of energy supply systems. Moreover, the stability and economy of these systems are mainly affected by the operation strategy and the capacity allocation scheme of each device. Therefore, a novel hourly dynamic simulation model is established to obtain the optimal capacity allocation scheme. The novelty is that it considers the reduction in CO2, NOx and SO2 emissions. The variable load operation characteristics of the main devices are considered. With the operation strategy of following the electrical load and following the thermal load, the model is solved by nondominated sorting genetic algorithm II with the optimization objectives, including economy, environment and energy savings. The technique for order preference by similarity to an ideal solution method is used as a multiobjective decision method. As a result, with the following the electrical load strategy, the pollutant emission reduction rate is 74.86%, the primary energy savings rate is 36.83%, the annual cost savings rate is 13.04%, and the comprehensive index is 54.89%. When the electrical cooling ratio is 0.8, it is 0.29% higher than the pollutant emission reduction rate without mixed refrigeration and 0.63% higher than the primary energy savings rate. When the optimal capacity of the photovoltaic system is configured, the pollutant emission reduction rate increases by 5.13%, the primary energy savings rate increases by 15.59%, the annual cost savings rate increases by 7.48%, and the comprehensive index increases by 7.51%. Therefore, the novel model and optimization algorithm are reliable and feasible for addressing the system capacity configuration improving system operation stability.

Optimized wind-light-storage configuration based on Homer pro

With the increasing shortage of energy and increasing demand for electricity, renewable energy microgrids have become the future trend of power grids. However, due to the strong uncertainty of its power generation, improving the stability of power system operation and reducing the cost of microgrid configuration have become important issues for the optimization of microgrid configuration. To address the above problems, this paper proposes a wind-light storage system, and establishes a model for optimal allocation of microgrid capacity with the minimum net present value of this system as the objective function. The relevant parameters are entered into Homer Pro software, which performs simulation calculations to obtain the configuration solution with the best economy and performance.

Capacity coordination under uncertain yield for supply chains with a single supplier and two competing retailers

The product’s yield rates of somewhat unpredictable due to their innovative and sophisticated manufacturing processes, and as a result, the demand for downstream supplier chain members may not always be satisfied. Therefore, contractual agreements are often established to facilitate order fulfilment between suppliers and retailers and thus sustain their cooperative partnerships. This study considers a supply chain, in which a single supplier cooperates with long-term and newly cooperating retailers and develops a game-theoretic model to investigate the optimal capacity allocation strategy for the supply chain. In considering the supplier’s uncertain capacity and competition from the other rival retailer, both retailers will tend to order more than they actually need to avoid possible shortage loss. However, this may strengthen the bullwhip effect and damage the effectiveness of the supply chain. A strategic capacity allocation mechanism with contractual agreements is adopted to prevent distortion of order quantities and also to ensure that the retailers can receive their minimal acceptable quantities. In addition, it is determined that revenue-sharing coordination will mitigate the bullwhip effect and enhance profits for the entire supply chain. The results indicate that the proposed approach will make it possible for the long-term cooperating retailer to honestly place orders.

Optimising the balance of acute and intermediate care capacity for the complex discharge pathway: Computer modelling study during COVID-19 recovery in England.

ObjectivesWhile there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for ‘step down’ intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions.MethodsDemand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays.ResultsIncreased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute ‘bed blocking’ is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway.ConclusionsComputer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code.

Multi-objective optimal capacity allocation of integrated energy system with co-evolution mechanism

Abstract With the deepening of multi-energy flow coupling and the diversification of energy utilization demand, integrated energy system (IES) puts forward higher requirements for the economy and reliability of capacity allocation scheme. However, there is a complex relationship between economy and reliability, which makes it difficult for them to achieve coordinated optimization. Therefore, an economic and reliability optimal capacity allocation method of IES based on improved co-evolution algorithm is proposed. Firstly, the timing scenario reduction method of “wind power-electrical load” based on ordered clustering algorithm and K-means clustering algorithm is proposed. Aiming at minimizing the expected annual cost of construction investment, operation and maintenance, carbon emission and energy purchase cost, and constrained by equipment operation and energy flow balance, a multi-scenario economic operation model is constructed. Considering the dissatisfaction degree of comprehensive energy consumption, the energy supply reliability evaluation model based on Markov chain Monte Carlo (MCMC) method is established. On this basis, the IES multi-objective optimal capacity allocation model is established. Further, based on the orthogonal test method, the influence factors of decision variables on IES economy and reliability are calculated, and the evolution subpopulation of economy and reliability is established, and then the optimization model’s solution method based on improved co-evolution algorithm is proposed. Finally, an example simulation analysis is carried out combined with the actual data of a certain region. The simulation results show that the proposed model can cooperatively improve the economy and reliability of IES capacity allocation scheme, and provide a reference for the optimal capacity allocation of equipment in IES.

IASA-Based Capacity Allocation Optimization Study for a Hydro–Pumped Storage–Photovoltaic–Wind Complementary Clean Energy Base

Photovoltaic and wind power is uncontrollable, while a hydro–pumped storage–photovoltaic–wind complementary clean energy base can ensure stable power transmission in the whole system through power quantity regulation by the hydropower station and the pumped storage station. Reasonable allocation of installed capacities of various power sources in the system can improve the reliability and economy of systematic power supply. A system model was built generalizing the hydropower station and the pumped storage station as an energy storage unit, compensating and regulating the natural output process to match the system output and the load and to establish a correlation between the installed capacity of the base and the output index. Installed capacity allocation optimization was studied through an optimization model built with an initial investment of the base as the objective function and with power supply guarantee rate, power abandonment rate, and installed capacity as restraints and solved using improved artificial sheep algorithm (IASA) based on the shepherd dog supervision mechanism. A Yellow River clean energy base was selected for a case study analyzing the influence of power supply guarantee rate and power abandonment rate on installed capacity allocation and investment. For the two most important parameters in the optimization process, that is, the power supply guarantee rate and the power abandonment rate, after qualitative and quantitative analysis, it is found that the power supply guarantee rate has a greater impact on the initial investment. In this study, a combination of the power abandonment rate of 18% and the guaranteed rate of 90% is finally selected for the optimization calculation. The case study indicates that sole increase of installed photovoltaic or wind capacity resulted in the increase of both power supply guarantee rate and power abandonment rate; an appropriate increase in the installed capacity of the pumped storage station raised the power supply guarantee rate and lowered the power abandonment rate; and the optimal installed capacity allocation of the photovoltaic, wind, pumped storage, and hydropower under a specific load condition of the case project is 4.6:1.4:1.7:1.

Optimal Allocation of Finite Sampling Capacity in Accumulator Models of Multialternative Decision Making.

When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here, we study the problem of how to optimally allocate limited sampling time to multiple options, modeled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sublinearly, closely following a power law as a function of capacity for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multialternative decisions are endowed with breadth–depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities.