Proposed Pricing Scheme Research Articles

Optimising ride-sharing efficiency: innovative shareability-focused pricing strategies

Ride-sharing is increasingly recognised as a potential solution to transportation issues such as traffic congestion and parking problems. The success of ride-sharing systems depends on the willingness of individuals to share rides, and effective pricing strategies are among the crucial factors. However, creating a fair pricing system for riders and providers remains challenging. This research introduces a new pricing scheme based on the concept of shareability. This concept considers all the possible sharing situations among trips. It is designed to incentivize riders and accommodate providers' criteria. This scheme calculates the trip fare for passengers, considering various ride-sharing scenarios derived from the shareability function, thus ensuring fairness and transparency for passengers. Using the macroscopic fundamental diagram, our model incorporates traffic dynamic conditions. We have applied the method to the real case of Lyon city in France to assess the effectiveness of our approach. Through a comprehensive analysis of various configurations of the proposed pricing scheme and benchmarking with the existing approaches, we demonstrate its superior performance in reducing ride rejections compared to traditional methods. This result showed the proposed method's potential for enhancing ride-sharing systems' effectiveness.

Design an intermediary mobility-as-a-service (MaaS) platform using many-to-many stable matching framework

Mobility-as-a-service (MaaS) provides seamless door-to-door trips by integrating different transport modes. Although many MaaS platforms have emerged in recent years, most of them remain at a limited integration level. This study investigates the assignment and pricing problem for a MaaS platform as an intermediary in a multi-modal transportation network, which purchases capacity from service operators and sells multi-modal trips to travelers. The analysis framework of many-to-many stable matching is adopted to decompose the joint design problem and to derive the stability condition such that both operators and travelers are willing to participate in the MaaS system. To maximize the flexibility in route choice and remove boundaries between modes, we design an origin–destination pricing scheme for MaaS trips. On the supply side, we propose a wholesale purchase price for service capacity. Accordingly, the assignment problem is reformulated and solved as a bi-level program, where MaaS travelers make multi-modal trips to minimize their travel costs meanwhile interacting with non-MaaS travelers in the multi-modal transport system. We prove that, under the proposed pricing scheme, there always exists a stable outcome to the overall many-to-many matching problem. Further, given an optimal assignment and under some mild conditions, a unique optimal pricing scheme is ensured. Numerical experiments conducted on the extended Sioux Falls network also demonstrate that the proposed MaaS system could create a win-win-win situation—the MaaS platform is profitable and both traveler welfare and transit operator revenues increase from a baseline scenario without MaaS.

Federated data acquisition market: Architecture and a mean-field based data pricing strategy

With the increasing global mobile data traffic and daily user engagement, technologies, such as mobile crowdsensing, benefit hugely from the constant data flows from smartphone and IoT owners. However, the device users, as data owners, urgently require a secure and fair marketplace to negotiate with the data consumers. In this paper, we introduce a novel federated data acquisition market that consists of a group of local data aggregators (LDAs); a number of data owners; and, one data union to coordinate the data trade with the data consumers. Data consumers offer each data owner an individual price to stimulate participation. The mobile data owners naturally cooperate to gossip about individual prices with each other, which also leads to price fluctuation. It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario. Hence, we propose a data pricing strategy based on mean-field game (MFG) theory to model the data owners’ cost considering the price dynamics. We then investigate the interactions among the LDAs by using the distribution of price, namely the mean-field term. A numerical method is used to solve the proposed pricing strategy. The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme. The result further demonstrates that the influential LDAs determine the final price distribution. Last but not least, it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.

A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems

Demand response (DR) is an attractive concept that invites customers’ active participation in the distribution sector by means of price elasticity of demand (PED). It not only enhances customers’ demand sensitivity but also improves technicalities and economics related to both the utility and demand sides. This paper emphasizes the combined effect of price-based DR (PBDR) and incentive-based DR (IBDR) with the inclusion of PED. The elasticity phenomenon, when applied with incentives as in IBDR, changes the demand-consumption pattern as compared to individual DR. Moreover, the demand variation due to only incentives leads to incentive elasticity, which needs to be studied carefully; then only the impact of individual DR and augmented DR (PBDR and IBDR combined) can be understood analytically. In this work, IBDR models are tested on considered pricing schemes along with a new proposed pricing scheme to evaluate the systems’ technical and economical parameters. A standard IEEE 33 bus distribution system has been chosen for the assessment of suggested models and to compare them to the existing ones. Furthermore, these models are descriptively evaluated from both the utility and consumer perspectives.

Incentive Compatible Pricing for Enhancing the Controllability of Price-Based Demand Response

Price-based demand response is widely implemented by the load aggregators (LAs) to exploit the flexibility of massive demand-side resources. However, a key problem in real-life implementation is the controllability of price-based demand response, viz, whether the price can guide the demand to the desired value. To bridge this gap, this paper proposes a close-loop reverse Stackelberg game-based pricing method to better regulate the demand response. In the hierarchical game structure, the non-cooperative competition of heterogeneous demand-side resources in participating demand response constitutes the lower-level, while the determination of pricing policy by LA makes the upper-level. Towards a computationally efficient solution, the mean-field game is adopted to approximate the Nash equilibrium of the large-scale lower level competition, and the indirect formulation of close-loop reverse Stackelberg game is adopted. The accuracy of the approximation is proved to be the (1,2) -hierarchical equilibrium of the original game. Finally, a decentralized solution algorithm with sequential linear programming as outer-loop and mean-field iteration as inner-loop is proposed to price the demand response. Numerical tests on a retail electricity market containing 15,000 heterogeneous demand-side flexible resources validate the effectiveness of the proposed pricing scheme.

Multi-energy pricing strategy for port integrated energy systems based on contract mechanism

This article studies a pricing scheme to increase the profit of ports and gas utility company (GUC), and improve social welfare. Firstly, a system model of the port integrated energy system (PIES) is established and the government green subsidy on biomass gas is considered. Secondly, a multi-stage game is formulated to characterize the interaction between the port, GUC and the government. Besides, the contract mechanism is used to improve the operation performance of the energy supply chain. Then, the equilibrium solutions of the game are obtained by the backward induction. Finally, the theoretical analysis and simulation show that the proposed pricing strategy can improve the profit of GUC, ports and government, and the proposed revenue sharing contract mechanism can coordinate the energy supply chain.

An optimization-based partial marginal pricing method to reduce excessive consumer payment in electricity markets

Among the pricing schemes, locational marginal pricing is the most widely adopted scheme in current electricity markets. Despite the nice properties, theoretical study and practical experience have shown that considering the strategic bidding behaviors of market participants, locational marginal pricing may result in excessive consumer payment and prejudices market efficiency. In this paper, we propose an optimization-based partial marginal pricing method to reduce consumer payment under strategic bidding. The advantages of the locational marginal pricing method are reserved to the best extent while the remaining problems are delicately handled. To achieve this, we first analyze the characteristics and problems of the locational marginal pricing scheme and present a partial marginal pricing structure. Second, we establish an optimization-based partial marginal pricing model, in which discriminatory price components are introduced in price formulation and the pricing properties including competitive equilibrium and revenue adequacy are formulated as constraints. Third, to verify the performance of the proposed pricing scheme considering the strategic bidding, a bi-level strategic bidding problem of the generator under the proposed pricing method is established and transformed into a mixed integer linear programming problem. The numerical results indicate that under the conventional pricing methods, the consumer payments under strategic bidding can be up to 16.67 times higher than those under truthful bidding, while the proposed method can effectively reduce excessive consumer payments by 92.1% while maintaining the desired pricing properties.

Implementation of DC net metering scheme in off-board EV charging systems with a time of usage capability

Dynamic pricing is one of the most significant aspects of strengthening the existing power grid. The widespread adoption of electric vehicles (EVs) increases the average electricity demand and peak power consumption. Meanwhile, the uncoordinated EV charging makes the condition even more severe by widening the gap between power supply and demand. Consequently, it demands the reinforcement of the current electricity network. However, a net-metering policy based on time of usage (TOU) pricing helps in demand side management including vehicle-to-grid (V2G) scheduling. It reduces the impact of peak load and provides incentives for all EV owners and utility operators. In this research, a problem has been formulated to reduce the electricity bill with TOU-critical peak pricing (CPP)/peak time rebate (PTR) based tariff structure to coordinate the EV charging. The economic analysis of the flat and proposed pricing structure has been discussed with the help of case studies. Further, the DC net meter designed and developed by the authors has been tested on the proposed pricing scheme to minimize the losses and improvise the load curve.

Customized Pricing Strategy for Households Based on Occupancy-Aided Load Disaggregation

Pricing strategies can be utilized to manage energy demand-supply imbalance and power distribution network congestion. With the application of smart meter technologies, the real-time consumption behavior of individual households can be studied to enhance the effectiveness of the formulated pricing strategy. Hence, in this paper, a customized pricing strategy based on non-intrusive load monitoring (NILM) techniques is proposed from the perspective of the energy retailer, aiming to incentivize households to change their load consumption patterns. First, the real-time occupancy state of each household is detected to identify the ability of the household to respond to the retail price. Second, the occupancy-aided factorial hidden Markov model (FHMM) is developed to facilitate the retailer to incorporate the real-time consumption behavior of households into pricing strategy formulation. Third, the real-time price elasticity of the consumption of each appliance within each household is mathematically modeled based on the real-time hidden state of each appliance. Numerical results indicate that the proposed pricing strategy is effective in managing the load consumption of households and assisting the retailer in increasing profitability.

Customised Multi-Energy Pricing: Model and Solutions

With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated.

Pricing for Electric Vehicle Charging Stations Based on the Responsiveness of Demand

Electric vehicles (EVs) have the promising potential to be effective in mitigating greenhouse gas emissions in the transportation sector. Hence, the penetration of EVs in some countries experiences steady growth. However, the proliferation of EVs can cause negative impacts on the power distribution network (PDN) and the transportation network (TN). Thus, in this paper, a dynamic pricing strategy for electric vehicle charging stations (EVCSs) is proposed to facilitate EVCSs to earn higher profits while alleviating the potential negative impacts on both PDN and TN. First, a pricing strategy, considering the competition effect established based on the TN, is formulated to facilitate the EVCS to attract the defined competitive charging demand. Second, a two-step approach is proposed to mathematically formulate the responsiveness of demand towards the charging price. Third, EV user behaviors are incorporated based on both an admission control scheme and a queueing model to further adjust the charging demand. We have conducted simulations to verify the effectiveness of the pricing strategy in a PDN and TN coupled system, which contains approximately 2000 EVs. Results show that the proposed pricing strategy can facilitate the EVCS to gain 14.2% more net charging profit on average compared with the other three cases. Moreover, the operational stability of the EVCS can be ensured because the proposed pricing strategy can result in the least queueing length volatility with moderate profit variance compared with the other three cases. Furthermore, the proposed pricing strategy can achieve spatial load shifting by incentivizing EVs to alter their station-selection decisions to avoid possible power congestion in the electricity network.

Collaborative Pricing in a Power-Transportation Coupled Network: A Variational Inequality Approach

This paper proposes a new collaborative pricing scheme for a power-transportation coupled network based on the variational inequality (VI) approach. In the proposed scheme, nodal electricity prices and congestion tolls on roads and at charging stations are considered to coordinate the coupled networks in order to minimize the operational cost of the whole system. The prices are determined by a second-order cone-based AC power flow model and a mixed user equilibrium model, respectively. A collaborative pricing model (CPM) is then built based on the two models and the interactions between them. In order to avoid the intractability of the developed non-convex model, the CPM is transformed into the VI formulation. With proven existence and uniqueness of solutions of the VI formulation, a new prediction-correction algorithm is proposed to accelerate the solution of the CPM problem, which is guaranteed to converge to the optimal solution. The proposed models and algorithm are verified using case studies on a real-world test system. The results show that the proposed pricing scheme can reduce the operational cost and the proposed algorithm shows improved convergence and higher computation efficiency compared with the existing algorithms.

An optimal real-time pricing strategy for aggregating distributed generation and battery storage systems in energy communities: A stochastic bilevel optimization approach

The expansion of distributed electricity generation and the increasing capacity of installed battery storage systems at the community level have posed challenges to efficient technical and economic operation of the power systems. With advances in smart-grid infrastructure, many innovative demand response business models have sought to tackle these challenges, while creating financial benefits for the participating actors. In this context, we propose an optimal real-time pricing (ORTP) approach for the aggregation of distributed energy resources within energy communities. We formulate the interaction between a community-owned profit-maximizing aggregator and the users (consumers with electricity generation and storage potential, known as “prosumagers”, and electric vehicles) as a stochastic bilevel disjunctive program. To solve the problem efficiently, we offer a novel solution algorithm, which applies a linear quasi-relaxation approach and an innovative dynamic partitioning technique. We introduce benchmark tariffs and solution algorithms and assess the performance of the proposed pricing strategy and solution algorithm in four case studies. Our results show that the ORTP strategy increases community welfare while providing useful grid services. Furthermore, our findings reveal the superior computational efficiency of our proposed solution algorithm in comparison to benchmark algorithms.

Dynamic Discrimination Pricing and Freelance Drivers to Rebalance Mixed-Fleet Carsharing Systems

This paper presents pricing strategies for the relocation problem in mixed carsharing systems with heterogeneous users. On the demand side, a dynamic discriminatory pricing approach is introduced, which can obviate the need for operation-based repositioning, partially or fully. Moreover, it’s assumed that there are earnings-sensitive freelance drivers who may decide to participate in relocation operations, and their decisions depend on the wages are offered by the operation manager. As the operational decisions (trip pricing, relocators hiring, and charging scheduling) are tightly connected, and their interplay can significantly affect the performance of the carsharing system, an integrated model is proposed to optimize trip price and staff wage problems along with vehicle allocation and charge scheduling. To cope with computation difficulties for solving the proposed model, this paper presents a solution framework based on a rolling horizon approach. Through extensive numerical experiments, the performance and effectiveness of the proposed model are investigated under different assumptions and parameter settings. The results confirm that the proposed pricing strategies outperform common strategies in the literature and can significantly improve the performances of carsharing systems. The sensitivity analysis of several key parameters provides managerial insights into the operations management of carsharing systems.

Selective and optimal dynamic pricing strategy for residential electricity consumers based on genetic algorithms

The continuous increase in residential population implies an increase in electricity demand. The demand prediction performed by the utility company (UC) to schedule and plan the next energy purchase from the market may not be efficient since the actual demand consistently exceeds the supply. To use the energy production infrastructure efficiently and decrease the grid system's overload, the UC seeks to match the demand with the supply. Dynamic pricing is a simple yet effective way to influence consumption as price-sensitive consumers may reschedule the operation of some of the appliances. This paper presents a novel, consumption data-driven, selective and dynamic Time-of-Use (ToU) electricity pricing strategy applicable by the UC for residential electricity consumers. The method consists of clustering real consumption data using the k-means clustering technique and heuristically categorizing clusters based only on their consumption data to simplify the design of the ToU tariffs and limit the number of different tariffs in the same population. The method includes a heuristic determination of the time period for the ToU tariff changes for each category. The ToU tariff parameters are determined by minimizing a single cost function using genetic algorithms and considering all consumer clusters such that the consumer behavior model is based on price elasticity. The optimal ToU tariffs result in decreasing the demand in power peaks by targeting the overconsumer categories in the latter. Implementing the ToU tariff results in a profit margin distributed among the UC and the consumer. Moreover, the optimal prices guarantee positive gains for both of them with the presence of a wide range of parameter uncertainties. The proposed pricing strategy necessitates the presence of consumption data only, which implies the conservation of the consumers' privacy. Real consumption data obtained for two towns in Hungary over three years is used to show the efficiency of the proposed pricing strategy.

Differential Pricing-Based Task Offloading for Delay-Sensitive IoT Applications in Mobile Edge Computing System

With the evolutionary development of the Internet of Things (IoT), the demand for delay-sensitive applications has increased. In this manner, mobile-edge computing (MEC) has emerged as a promising technology to run these applications on mobile devices. Several studies have been conducted on pricing schemes in the MEC environment, and they have focused on the amount of offloaded data in pricing. However, under the previously proposed pricing policy, the server resource usage of users is not properly reflected in the payment, so users only try to occupy the server resources as much as possible without any consideration. To solve this problem, this article newly proposes a differential pricing scheme in which the unit price per second is determined based on the user’s usage of server computational resources. Also, it suggests the user’s optimal offloading strategy and server’s equilibrium pricing strategy by formulating Stackelberg game based on the proposed pricing scheme and execution delay. Several numerical results show that the proposed pricing scheme and strategies can solve the problems and can increase the efficiency of edge server’s computational resources.

Diff-Price: Differential Pricing Scheme for Provisioning Safety-as-a-Service in Vehicular IoT Applications

In this paper, we propose a differential pricing scheme, named as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diff-Price</i> , to deliver customized safety services to the users at an optimal price and maximizing the profit of Safety Service Provider (SSP) using the Safety-as-a-Service (Safe-aaS) platform. None of the existing pricing schemes addresses the dynamic variation of price as the type of users and decision parameters requested by them vary. We assume road transportation as the implementation scenario of Safe-aaS. In order to deal with these issues, we formulate a pricing scheme, which renders different prices for the same decision parameters. The amount charged from the users are based on their type, decision parameters selected by them, and the duration for which they request the service. Additionally, the SSP allows suitable discounted price to the users, which is determined from their mode of payment and history of services availed. Further, to model the interaction between the users and SSPs, we design our problem as a multiple leader multiple follower non-cooperative Stackelberg game. We calculate the utility of SSPs and formulate an optimization problem to compute the optimal value of the basic price charged from the users. The exhaustive mathematical analysis and simulation results reveal that the proposed pricing scheme, Diff-Price improves the financial gain of the service providers as the number of users increases by 13.51%, 15.75%, and 14.173% respectively, compared to the adaptive incremental spot pricing scheme [1], differentiated cloud virtual-network as-a-service scheme [2], and smart road pricing system [3].

Pricing Strategy of Cold Ironing Services for All-Electric Ships Based on Carbon Integrated Electricity Price

Maritime transportationis currently responsible for about 90% of the worldwide trade and contributes about 2.7% of the global carbon emission. To establish a sustainable maritime system, transportation electrification is an inevitable trend. Under this context, cold ironing technology for all-electric ships (AESs) emerges. In this paper, we propose a tri-level pricing framework of cold ironing services for AESs, which comprehensively considers the voyage scheduling of AESs, the power system dispatch, and the profit of the cold ironing serving entity (CISE). Time-of-use (TOU)-based pricing, where price level and price structure are jointly optimized, is formulated based on the carbon integrated distribution locational marginal pricing (DLMP). The proposed pricing strategy is verified in a three-seaport system. It is found that compared with the real-time pricing strategy and the flat pricing strategy, the proposed method can better incentivize the AESs to realize power congestion management and emission control. Besides, it guarantees the profit of CISE with low risks and helps the customer to hedge the market risks.

Peak-to-average ratio incentive scheme to tackle the peak-rebound challenge in TOU pricing

Time of use (TOU) pricing scheme reduces the peak demand, and electricity procurement costs by shifting flexible loads from peak to off-peak hours. The electricity price is known to the end-users a day ahead or a season in TOU pricing. When penetration of home energy management system (HEMS) and automation in a distribution system is high, the TOU pricing may cause peak rebounds in the grid load profile due to deferrable load accumulation during low price periods. It may cause suppliers’ economic loss, power imbalance, voltage violations, increased power loss in the network, and may overload the transformers and distribution lines. This paper proposes a pricing scheme for residential customers to eliminate the deferrable load accumulation during low price periods of TOU pricing. The proposed pricing scheme considers peak to average ratio (PAR) incentive on energy consumption charge for each price period of TOU pricing. The PAR incentive is included in HEMS at customers’ premises and supplier’s price calculation. The proposed PAR incentive scheme is implemented in 200 houses with different appliances and different energy requirements. The proposed PAR incentive scheme successfully avoids the deferrable load accumulation during low price periods of TOU pricing. The cost of power procurement in the proposed PAR incentive scheme is found lesser than other compared schemes for load profiles with and without solar PV. The proposed incentive scheme also reduces the electricity bills of the customers.

Stochastic Control and Pricing for Natural Gas Networks

We propose stochastic control policies to cope with uncertain and variable gas extractions in natural gas networks. Given historical gas extraction data, these policies are optimized to produce the real-time control inputs for nodal gas injections and for pressure regulation rates by compressors and valves. We describe the random network state as a function of control inputs, which enables a chance-constrained optimization of these policies for arbitrary network topologies. This optimization ensures the real-time gas flow feasibility and a minimal variation in the network state up to specified feasibility and variance criteria. Furthermore, the chance-constrained optimization provides the foundation of a stochastic pricing scheme for natural gas networks, which improves on a deterministic market settlement by offering the compensations to network assets for their contribution to uncertainty and variance control. We analyze the economic properties, including efficiency, revenue adequacy, and cost recovery, of the proposed pricing scheme and make them conditioned on the network design.