Station Revenue Research Articles

Considering Differentiated Pricing Mechanisms for Multiple Power Levels

With the development of supercharging technology, charging stations will face supercharging piles and fast-charging piles coexisting for a long time. The traditional unified service fee pricing model will face the problem of slow-charging vehicles occupying supercharging piles, and the advantages of supercharging facilities cannot be fully utilized. Considering factors such as station revenue, station utilization rate, and user willingness, this paper constructs a differentiated pricing model for multi-power-level charging facilities based on capacity and electricity service fees. It reveals the impact of this pricing model on the economy and user satisfaction of charging stations. In addition, a multi-objective optimization model is constructed and solved by a multi-objective particle swarm method to determine the pricing strategy of different power levels that adapt to the actual situation. The results show that the differentiated pricing mechanism can increase the station revenue by about 10.74% and the station stack power utilization rate by about 7.14%, and user satisfaction is higher, which is better than the conventional pricing mechanism. Therefore, the proposed charging pricing strategy can provide a decision-making reference for charging station operation and planning.

Smart Slot Assignment for Urban EV Charging Using Folium API Integration

Abstract: Efficient management of electric vehicle (EV) charging distribution in urban areas is crucial amid increasing demand and varying traffic conditions. This study proposes a novel web-based solution that integrates Folium Map API for realtime location tracking and Python Flask backend with MySQL database storage to allocate charging demands. By leveraging real-time traffic information from the Folium Map API, the proposed model optimizes charging resource allocation across multiple stations and road networks. The web interface, built using HTML, CSS, and JavaScript, allows users to interact with the system seamlessly. Charging allocation is dynamically adjusted based on real-time data, prioritizing maxi- mum returns. Additionally, an emergency charge counter facility is introduced to cater to unforeseen charging needs, enhancing user flexibility. The system offers two interfaces and a dashboard for both station administrators and users, en- abling pre-booking services for travelers during their journey, facilitating access to nearby charging stations, and providing a facility for prebooking. These features contribute to improved station revenue and pile utilization, highlighting the efficacy of the proposed web-based approach. By leveraging advanced web technologies and APIs, this comprehensive strategy enhances charging infrastructure efficiency, fosters the widespread adoption of electric vehicles, and contributes to sustainable urban mobility management.

Benefit sharing model of hydropower development based on input perspective: a case study of Xiluodu project in China

Hydropower provides important support for achieving dual-carbon goal, but problems such as reservoir resettlement conflicts are common in developing countries, which have become a bottleneck of sustainable hydropower development. Fair distribution of benefits is the key to solve this dilemma. While the concept of benefit sharing as the fundamental guarantee for the sustainable livelihood of landless resettlers is gradually reaching consensus, the amount of benefit distribution has always been an outstanding issue. In order to quantify the amount of benefit sharing scientifically and reasonably, this paper constructs a benefit sharing model of hydropower development from the perspective of stakeholder input, and carries out a case analysis. The results indicate that the main stakeholders of hydropower projects include governments, development enterprises and reservoir resettlers. Their inputs are water or environmental resources, capital, and land resources, which produce hydropower resources. The net present value method can be used to construct the benefit sharing model. In this model, sharing amount of resettlers and other major stakeholders is the product of the sum of the present value of the net cash flow of hydropower projects and the proportion of their respective input. In view of many uncertainties, the grey prediction method and Monte Carlo simulation method are used to solve the model. The case of the Xiluodu hydropower station in China proves the rationality and operability of the model. The study also shows with reference to the model’s calculation results, a shared development fund can be established by drawing money from electricity generation income to ensure long-term benefits for resettlers, while considering the reasonable benefit of other stakeholders. The two are connected. It is expected to achieve a new pattern of mutual benefit and multi-win outcome, which are characterized by stable power station revenue, prosperity and stability of the reservoir area, and increased benefit and common wealth of resettlers. This paper may provide a convenient and novel analysis tool for benefit sharing systems.

Analysis of Optimal Operation of Charging Stations Based on Dynamic Target Tracking of Electric Vehicles

In view of the large impact of traditional charging stations on the power grid and the investment in the construction of charging stations for electric vehicle infrastructure services, this paper considers the configuration of optical storage equipment in charging stations from a practical point of view and proposes an economic operation strategy for charging stations to meet the economically optimal requirements of different scenarios. First, we analyze the behavioral characteristics of multiple types of electric vehicles, consider the influence of charging queues, and establish a daily load model of charging stations by taking into account the daily monitoring load and nighttime lighting load of charging stations. Then, considering the electric vehicle (EV) charging demand, photovoltaic (PV) output and energy storage charging and discharging power, the daily economic optimal operation problem based on the dynamic target tracking of charging stations is established; the objective is to maximize the daily operating revenue of charging stations under the condition of satisfying the EV charging demand and PV consumption. Secondly, the objective function is linearized, and the economic operation model is transformed into a mixed integer linear programming model for solving, and the simulation is verified under different scenarios. The results show that the economic optimal operation strategy can adapt to the economic operation requirements of charging stations in different scenarios and maximize the charging station revenue.

Can AI Abuse Personal Information in an EV Fast-Charging Market?

In order to alleviate the range anxiety of electric vehicle users (EVUs), several researches focus on facilitating the efficiency of fast-electric vehicle charging stations (fast-EVCSs) using artificial intelligence (AI). This paper first proposes a fast-EVCS revenue maximization pricing policy using an AI approach, and we argue that the AI algorithm can learn to abuse EVUs information for maximizing its revenue. In order to investigate the hypothesis, firstly, a simulation environment is developed using vehicle performance models and an EVU’s charging station selection game. Then, we formulate the charging station revenue maximization problem as a Markov decision process (MDP) and propose a personalized dynamic pricing policy using a model-free reinforcement learning (RL) algorithm. From numerical simulation results, it is found that if the RL approach focuses solely on increasing revenue of the fast-EVCSs, it can learn to misuse personal information without any human intervention. To prevent such abuse, we suggest intuitive guidelines for policymakers and urban planners via numerical experiments.

Star Wars? Space Weather and Electricity Market: Evidence from China

This paper aims to investigate the impact of space weather on China’s electricity market. Based on data products provided by NOAA and the National Energy Administration in China, this paper uses solar wind velocity as a solar weather indicator and the disturbance storm time index as a magnetospheric weather indicator to match monthly Chinese electricity market data over 10 years. Based on a VAR model, we found that (1) space weather increases the demand for electricity in China, and solar wind speed and the geomagnetic index increase the electricity consumption of the whole of Chinese society, as space weather mainly increases the electricity consumption of the secondary and industrial sectors. (2) The geomagnetic index significantly promotes power station revenue. (3) Space weather is associated with increased energy consumption. The geomagnetic index significantly increases the coal consumption rate of fossil power plants in China, but the solar wind speed has nothing to do with the coal consumption rate of fossil power plants.

Dynamic modeling and real-time management of a system of EV fast-charging stations

Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. Studies suggest that fast-charging facilities are crucial for the EV market. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers station charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each EV fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity (public or private) and that the demand for these stations are interrelated (through the station choice model). To manage the system of stations, this study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The computational results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations.

Simulation analysis of flexible concession period contracts in electric vehicle charging infrastructure public-private-partnership (EVCI-PPP) projects based on time-of-use (TOU) charging price strategy

It is critical to provide coordinated charging facilities along with the rapid growth of Electric vehicles (EVs). Public-Private-Partnerships (PPP) have been used to build and operate electric vehicle charging infrastructure (EVCI). However, stakeholders such as operators, users and governments have not obtained satisfactory benefits using fixed concession period PPP contracts. To promote revenue and encourage the participation of the private sector, this paper simulates the use of flexible concession periods for EVCI-PPP projects using an integrated System Dynamics (SD) model. The model involves a peak-valley time-of-use tariff (TOU) price strategy, while a price reduction strategy is designed to redistribute revenues between users and operators. Three scenarios are defined to optimize the flexible concession period: i) a unitary charging price reduction strategy, ii) an variable charging price reduction strategy, and iii) a government incentive strategy. The results indicate that a valley load period rebate price strategy can increase charging station revenue to a certain extent. Furthermore, redistributing operating revenue between operators and users could improve the efficiency of EVCI-PPP projects by obtaining a shorter payback period and reducing government subsidies. Our results have important policy implications.

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

Effect of shopping complex openings on nearby railway station revenue

Effect of shopping complex openings on nearby railway station revenue

Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode

A charging load allocation strategy for Electric Vehicles (EVs) considering charging mode is proposed in this paper in order to solve the challenge and opportunity of large-scale grid-connected charging under the background of booming EV industry in recent years. Based on the peak-to-valley Time-of-Use (TOU) price, this strategy studies the grid load, charging cost and charging station revenue variation of EVs connected to the grid in different charging modes. In addition, this paper proposes an additional charging mechanism for charging stations to encourage EV owners to participate in the peak and valley reduction of the grid through coordinated charging. According to the example analysis, under the same charging demand conditions, the larger EV charging power will have a greater impact on the grid than the conventional charging power. This article collects additional service fees for car owners who are not involved in the coordinated charging. When the response charging ratio is less, the more total service charges are charged, which can compensate for the decline in the sales revenue of the charging station during the valley period. While having good economy, it can also encourage the majority of car owners to participate in the coordinated charging from the perspective of charging cost.

Autonomous Energy Harvesting Base Stations With Minimum Storage Requirements

In this work, optimal energy and resource allocation for the downlink of an autonomous energy-harvesting base station is investigated. In particular, the joint maximization of the users' utilities and the base station's revenue is considered, while these hierarchical decision problems are matched to the framework of a generalized Stackelberg game. Also, an efficient iterative method is proposed that enables all the players to reach the variational equilibrium, i.e., the optimal solution of the game. Simulation results validate the effectiveness of the proposed method.

A Pricing Method for Small-size Cargo Express Service in Long Distance Highway Transportation

After a survey of 10 long-distance highway passenger transportation stations in Beijing, we found that 50% of the stations do not provide express services for small-size cargo. Passenger stations that provide such services have a low profit. The main reason is that there is no effective pricing method for small-size cargo express services. To address this issue, we first looked at the operating costs of all the parties involved in small-size cargo express services. According to the customers' freight transport demand function, we propose a model for determining the price of small-size cargo express services with an objective function of maximizing the starting station's revenue. Based on the model, necessary conditions are specified in order for passenger stations and companies to operate such businesses. Furthermore, a method of fitting the transportation demand function is provided based on actual operations and services. Finally, a numerical example is given to illustrate the application of the proposed pricing method for small-size cargo express services.

Joint admission and power control for quality-of-service in the wireless downlink

This paper addresses the problem of joint admission and power control in the downlink of a single-cell code-division-multiple-access (CDMA) wireless network. The objective is to maximize the user-capacity (or base station revenue), while meeting the quality-of-service (QoS) requirements of each admitted user. This paper considers bounded maximum delay and bounded minimum data rate as the QoS measures. For applications with strict QoS requirements, we show that the joint problem can be solved to optimality by performing admission control and power allocation separately. To solve the admission control problem, we give an exact polynomial-time algorithm for user-capacity maximization, and an efficient approximation algorithm with guaranteed performance for base station revenue maximization. To solve the power control problem, we provide two simple power allocation heuristics. For applications with soft QoS requirements, we consider the problem of allocating transmit powers to all users such that the minimum fraction of QoS requirement satisfied for any user is maximized. Regardless of the number of users in the system, we show that the problem always reduces to one-dimensional search. This leads an efficient power control algorithm that provides, in O(1) iterations, solutions arbitrarily close to the optimal one.

Commercial Broadcasting and Local Content: Cultural Quotas, Advertising and Public Stations

This article considers two radio stations choosing combinations of local and international content to broadcast to consumers with preferences over those combinations. Station revenue derives from sales of advertising time, the demand for which depends negatively on its price and positively on the station's market share and consumers get disutility from advertising. This article derives the laissez‐faire solution to this model and considers the consequences of a local content quota, an advertising cap and a non‐commercial public station for broadcasting diversity and welfare with and without an externality attached to local content.

The Audience-Revenue Relationship for Local Television Stations

Econometric analysis of television station revenue is undertaken to help assess the effects of changing cable television regulations on broadcast stations. Revenue is closely related to audience size and characteristics. Audience value depends upon its location relative to the station and differs according to time of day. It also differs between independent and affiliated stations and between UHF and VHF stations. To assess the effects of cable systems accurately, regulators need estimates of audience diversion by geographic location and by time of day. Independents, affiliates, UHF stations and VHF stations will be differently affected by given patterns of audience diversion.

Arriving at Television Advertising Rates

Abstract Advertisers depend more on profiled cost-coverage figures than on any other criterion when choosing television stations in local markets. The rates charged in a local market are strongly influenced by three factors—demand, competition and ratings—all of which are partially beyond a station's control. These three factors, tempered by a station's revenue goals and its aggressiveness in testing market rate resistance, form the underpinning of its rate setting procedure. This article, as the second of a two part series, contains guidelines for advertisers and agencies to evaluate quoted rates and create purchase strategies for different demand and competitive situations. These guidelines provide the sequel to a description of the process which stations follow in evaluating time and inventories and setting rates. The sequel appeared in the last issue of The Journal of Advertising, Vol. 7, No. 4.

Production and application of light: Annual report of the committee on production and application of light

Production of Light Statistics on Incandescent Lamp Sales THE sale of incandescent lamps held up remarkably well during the past year, large lamps showing a decrease of about 1 per cent as compared with 1929. The Central Station revenue derived from the two classes of service in which light is a large factor (domestic and commercial, small power and light) showed an increase of about 7.5 per cent as compared with the previous year, offsetting the reduction from other uses of electricity, and leaving a net increase of about 3 per cent.