Spatial Distribution Of Demand Research Articles

A novel formulation of low voltage distribution network equivalents for reliability analysis

Reliability analysis of large power networks requires accurate aggregate models of low voltage (LV) networks to allow for reasonable calculation complexity and to prevent long computational times. However, commonly used lumped load models neglect the differences in spatial distribution of demand, type of phase-connection of served customers and implemented protection system components (e.g., single-pole vs three-pole). This paper proposes a novel use of state enumeration (SE) and Monte Carlo simulation (MCS) techniques to formulate more accurate LV network reliability equivalents. The combined SE and MCS method is illustrated using a generic suburban LV test network, which is realistically represented by a reduced number of system states. This approach allows for a much faster and more accurate reliability assessments, where further reduction of system states results in a single-component equivalent reliability model with the same unavailability as the original LV network. Both mean values and probability distributions of standard reliability indices are calculated, where errors associated with the use of single-line models, as opposed to more detailed three-phase models, are quantified.

Station Location Optimization for the One-Way Carsharing System: Modeling and a Case Study

Carsharing is regarded as a new mode of transportation that can meet the diversity of travel demands. Carsharing systems have different operating modes, and one-way systems are more widely used since cars can be dropped off at any station. However, their planning involves a series of joint decisions regarding the number, size, and location of stations, as well as the fleet size. This paper develops a data-driven mixed-integer linear programming (MILP) model for planning one-way carsharing systems that consider the spatial distribution of demand and the interacting decisions between stations. The characteristics of existing stations and their spatiotemporal correlations are an important part of the model. To solve the MILP model, the extension of the Benders decomposition algorithm is adopted. The practicality of the proposed approach is demonstrated in a case study in Beijing, China. The results show that the existing planning of carsharing could result in a serious waste of resources. In contrast, the proposed method can obtain effective results in a reasonable time. The location results corresponding to a different rate of satisfied demand show that increasing the parking spots to improve the interaction between stations can effectively reduce the cost of operations. It should be noted that this paper only considers the benefit of operators. Future works will be carried out to optimize the one-way carsharing system by considering the benefits of operators, as well as the benefits of users and society. In addition, the impact of COVID-19 will be taken into account in future modeling and case studies.

Evaluation of heuristics for the p-median problem: Scale and spatial demand distribution

The objective of the p-median problem is to identify p source locations and map them to n destinations while minimizing the average distance between destinations and corresponding sources. Several heuristic algorithms have been developed to solve this general class of facility location problems. In this study, we add to the current literature in two ways: (1) we present a thorough evaluation of existing classic heuristics and (2) we investigate the effect of spatial distribution of destination locations, and the number of sources and destinations on the performance of these algorithms for varying problem sizes using synthetic and real datasets. The performance of these algorithms is evaluated using the objective function value, time taken to achieve the solution, and the stability of the solution. The sensitivity of existing algorithms to the spatial distribution of destinations and scale of the problem with respect to the three metrics is analyzed in the paper. The utility of the study is demonstrated by evaluating these algorithms to select the locations of ad-hoc clinics that need to be set up for resource distribution during a bio-emergency. We demonstrate that interchange algorithms achieve good quality solutions with respect to both the execution time and cost function values, and they are more stable for clustered distributions.

Supply and Demand Relationship of Taxi Battery Exchange Based on Big Data: A Case Study of Beijing, China

The power exchange mode is widely applied in the rental field as an efficient energy supply method for new energy vehicles. The power supply-demand relationship analysis swaps. In particular, the quantitative spatial analysis of sub-regions is of great significance for optimizing the spatial layout of power swapping stations, better operation of taxis, and more efficient power swapping stations. Therefore, this paper analyzes the correlation between the ten states of taxis and the corresponding power exchange. The present analysis targets the limitations in the existing methods to analyze the power exchange supply and demand and utilizing the big data pertaining to real-time taxi operation, order-taking mode, and station-swapping operation. As per the correlations, a calculation method is established to determine the power exchange demand based on the location where the orders are received and the matching method of the power exchange supply and demand. Besides verifying the scientific nature and feasibility of the method empirically, this study also ensured its great flexibility, which allows it to adapt to more complicated social scenarios. The big data analysis indicates that determining the spatial distribution of demand based on the location from where the taxi orders are received is far more rational and practical. Thus, this study has a vital role in guiding the location and layout of interchange stations.

A spatial‐knowledge‐enhanced heuristic for solving the p‐median problem

AbstractThe p‐median problem (PMP) is one of the most applied location problems in urban and regional planning. As an NP‐hard problem, the PMP remains challenging to solve optimally, especially for large‐sized problems. A number of heuristics have been developed to obtain PMP solutions in a fast manner. Among the heuristics, the Teitz and Bart (TB) algorithm has been found effective for finding high‐quality solutions. In this article, we present a spatial‐knowledge‐enhanced Teitz and Bart (STB) heuristic method for solving PMPs. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and service provision. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. This new algorithm can be incorporated into current commercial GIS packages to solve a wide range of location‐allocation problems.

Local-Level Site-Selection Model for Integrated Carsharing Services

Round-trip carsharing services, particularly when integrated with other transport options, have been proven to represent a viable means to mitigate the negative impacts of individual motorized traffic in dense urban areas. An increasing number of cities, therefore, aim to integrate carsharing with local public transport as an additional mobility option which promotes sustainable transport patterns. However, selecting appropriate sites for carsharing pods remains a difficult task for decision-makers since both integration with public transport as well as maximization of coverage of potential customers/market share have to be achieved. In this context, we present a GIS-based site-selection model that includes criteria from both the demand and the supply sides at the local level. On the demand side, the spatial distribution of demand is addressed by incorporating the spatial accessibility of local amenities and transport options, as well as socio-demographic and behavioural criteria. On the supply side, the key aspect is the integration of carsharing pods with public transport. Potential sites were assessed at the level of individual buildings, based on their proximity to public transport infrastructure, their centrality, and the residential locations of regular public transport users.

Distribution network rationalisation through benchmarking with DEA

PurposeThe purpose of this paper is to discuss the use of data envelopment analysis (DEA) to benchmark store performance for the purpose of rationalising retail distribution network.Design/methodology/approachAs an illustration of the approach, DEA is applied to a sample of front stores of a major retailer in Australia to compare their relative efficiency in distribution. Together with other techniques such as customer segmentation and spatial distribution of demand, this paper shows that DEA can provide an objective basis for distribution network rationalisation and be a suitable analytical tool to facilitate continuous improvement.FindingsBased on the DEA results, it is concluded that overall distribution efficiency of the part of the retail network under study can be improved by either closing the less efficient stores or merging them with the others in the same service areas to streamline the network. Such rationalisation will help aggregate demand and improve vehicle utilisation for distribution with minor impact on current level of customer service.Research limitations/implicationsThis study lends insight into the use of DEA, together with other analyses, for distribution network rationalisation. This approach is less data hungry and relatively easy to implement than full‐fledged optimisation through integer programming. To serve mainly as a proof of concept and an illustration of the approach, the scope of the study is limited to six stores in the retail network with relative performance in distribution evaluated on a single input and a single output variables.Practical implicationsManagers can use DEA to benchmark the distribution performance of their stores against the best performers in the retail network so as to identify areas for improvement. The approach can also assist in the adoption of best practice and facilitate more effective allocation of resources across the entire retail network.Social implicationsRetail network rationalisation through benchmarking with DEA can facilitate continuous improvement in distribution efficiency. This will help reduce fuel consumption, carbon emission, as well as other pollutions such as noise and traffic congestion.Originality/valueResearch in retail network performance using DEA to date is mainly on comparative performance of supermarkets within or between chains. The focus is mainly placed on the relationship between floor area, workforce, and sales. This paper fills the gap in the literature by applying DEA in distribution network rationalisation instead of mere performance comparison of individual stores. It focuses on distribution costs rather than store attributes and supplements DEA with other techniques to obtain a fuller picture of the overall network efficiency in terms of distribution. It also contributes to a better understanding of how demand management can affect distribution efficiency of the retail network.

Locational planning for emergency management and response: an artificial intelligence approach

The effi ciency of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely and effective manner upon an event’s occurrence. When dealing with public sector institutions, this refl ects the signifi cance for state or local offi cials to determine the optimal locations for emergency stations and vehicles. The typical methodology to deal with such a task is through the application of the appropriate location-allocation model. In such a case, however, the spatial distribution of demand although stochastic in nature and layout, when aggregated at the appropriate level, appears to be spatially structured or semistructured. Aiming to exploit the above incentive, a different approach will be examined in this paper. The spatial tracing and location analysis of emergency incidents is achieved through the utilisation of an Artifi cial Neural Network (ANN). More specifi cally, the ANN provides the basis for a spatiotemporal clustering of demand, defi nition of the relevant centres, formulation of possible future states of the system and fi nally, defi nition of locational strategies for the improvement of the provided services. The proposed methodological approach is applied to Athens Metropolitan Area and the adopted dataset constitutes of the incidents that were reported and confronted by the city’s Fire Department during the year 2008.

A model for facilities planning for multi-temperature joint distribution system

New technologies, such as replaceable cold accumulation and insulation box multi-temperature joint distribution (MTJD), provide precise temperature control, thereby reducing negative effects on food quality from exposure to extreme temperatures. This study compares conventional technologies with new ones, and constructs a binary integer-programming model to determine multi-temperature logistics techniques and food handling volume required for maximization of cost-efficiency in a hierarchical hub and spoke (H/S) network. It does so by minimizing the total delivery cost, comprised of terminal, food handling, and vehicle transportation costs computed, separately, by a derived algorithm. Appropriate technique(s) and handling volume for each terminal and vehicle routing are solved by Branch & Bound method, under the “best-first search” principle. The results indicate the model is feasible for facilities planning for MTJD. The replaceable cold accumulation and insulation box MTJD technique is suitable for operation networks with densely distributed terminals and uneven temporal and/or spatial demand distribution.

Regional wage and employment responses to market potential in the EU

In new economic geography models, the spatial distribution of demand is a key determinant of economic outcomes. In one strand, it is argued that higher demand gives rise to a more than proportionate increase in production, a result known as the home market effect. Another strand emphasizes the effects of market sizes on factor prices. We highlight the theoretical connection between these two strands. Using data on 57 European regions, we show how wages and employment respond to differentials in what we call real market potential, a discounted sum of demands derived from the theory.

Structural Change and Spatial Response in the Retail Sector in Germany

This study shows that in the past decades retailing in Germany experienced profound changes in the supply structure combined with massive shifts in shops' locations. Until the end of the 1950s the locational structure was decisively influenced by the spatial distribution of demand. Changes in demand, especially due to consumers' increased mobility, gave retail businesses a relatively free choice of locations. In more recent decades spatial development was mainly determined by changes in the types of retailing and their typical locational preferences. During this period, new non-integrated shopping locations developed and there were trends of thinning out in scattered locations and of a decrease in importance of the integrated subcentres. Since these shifts have adversely affected the centres' structure and have conflicted with social and political goals, planners and politicians have increasingly taken account of spatial development in retailing. They try to minimise the negative effects of the restructuring.

On Least‐Cost Design of Aqueduct Systems

A new approach to solve the problem of designing an aqueduct system in areas of uniform terrain slope is developed. The approach is based on the premise that, apart from the amount of water to be allocated, the location of the elements of an aqueduct system is a variable to be optimized for a minimum cost design. Based on the geographical distribution of the users and the individual demands, a function of demand is established to determine the optimal number of laterals. For each number of laterals, and using dynamic programming, the leastcost layout of the aqueduct system is determined. Then the overall minimum cost solution is determined. Simulation studies of hypothetical cases show that significant savings can be accrued to interest groups financing aqueduct systems through the use of mathematical optimization techniques, and that the least‐cost design depends on the spatial distribution of demand for water.

OPTIMAL SPATIAL TARIFFS OF JOINT PRODUCTS: THE PLANNING OF A COGENERATION AND DISTRICT‐HEATING PROJECT

ABSTRACT It is assumed here that utility districts supplying both electricity and heating, given their institutional and physical environments, can be considered in terms of continuous, spatially‐differentiated markets in which different consumers can be charged different prices for each of the joint products. An optimal spatial pricing rule is derived from a given spatial distribution of demand and income, a given normative function of distributional equity, and an assumption of spatially interdependent supply costs. In addition, the optimal size of the central cogenerating plant and the optimal size of the district are simultaneously derived. Finally, in the feasibility analysis, the possibility of cross‐subsidization is introduced.