Pricing Tariffs Research Articles

Problems in Tariff Policy for Freight Rail Transportation in Modern Conditions

JSC Russian Railways has a number of unresolved issues in the field of pricing and regulation of freight transportation tariffs. The company receives its main income from freight transportation. For 9 months of 2024, their volume amounted to more than 80%. Freight transportation provides the main financial position of the industry. The current regulatory framework for determining tariffs was formed in the 2000s. Recently, the rules for reforming railway tariffs have been actively discussed. At JSC Russian Railways, freight transportation prices are formed in such a way that the loading of low-income cargo is subsidized at the expense of high-income cargo. This leads to a reduction in the company's income and a decrease in expenses on the modernization and development of railway infrastructure. Existing problems in the field of pricing are considered.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Revisiting the role of thermal energy storage in low‐temperature electrified district heating systems

AbstractDecarbonising the energy supply system is crucial to mitigate climate challenges. An emerging type of the multi‐energy system, that is, the low‐temperature electrified district heating system is gaining increasing popularity as a potential solution for future low‐carbon heat supply. This paper investigated its operational optimisation with thermal energy storage (TES) installed at building sides. The optimisation model was to obtain the minimum operation costs of all heat pumps in this system. The TES was meant to achieve energy arbitrage through load shift, but it was observed from the optimised results that the TES did not play an active role in the optimisation. Five possible causes were identified and further investigated to reveal their impacts on the optimisation process. Results showed that the thermal capacitance, thermal resistance, and indoor temperature range of the building were major influencing factors, while the electricity price tariff and heat loss parameters of TES were minor ones. The results indicate that there is no need to equip the TES for operational optimisation purposes when the building thermal capacitance is larger than a threshold value, the thermal resistance is smaller than a threshold value, or the indoor temperature range is broader than a threshold value. These threshold values are case‐specific and can be determined with the simulation model and method developed in this paper.

A stochastic MPC-based energy management system for integrating solar PV, battery storage, and EV charging in residential complexes

This paper presents a Stochastic Model Predictive Control (SMPC)-based energy management system (EMS) for residential complexes with integrated solar photovoltaics (PV), battery energy storage systems (BESS), and electric vehicle (EV) charging infrastructure. The EMS coordinates BESS operations, integrating solar generation, residential load demand, and EV charging. It optimizes BESS charging/discharging based on solar power, load demands, electricity pricing, and feed-in tariffs over a finite horizon, while considering uncertainties through multiple scenarios of load and EV charging demand, as well as solar generation. By accounting for battery degradation, cost savings, and revenue from energy transactions, the proposed EMS enhances BESS longevity and profitability. The EMS also manages reactive power provision from the BESS inverter, ensuring voltage stability in the presence of uncertainties. Extensive case studies on Matlab Simscape Electrical and real-time validation on the OPAL-RT simulator demonstrate the effectiveness of the proposed SMPC-based EMS in optimizing energy use, operational efficiency, and economic returns, contributing significantly to the sustainable energy management of the residential complex.

The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64).

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

PCM as an energy flexibility asset: How design and operation can be optimized for heating in residential buildings?

In cold climates, residential buildings are often well-insulated to reduce heating costs but may lead to overheating. Incorporating Phase Change Material (PCM) into building envelope can serve as an effective measure to mitigate overheating and reduce cooling costs. However, the inappropriate incorporation of PCM in well-insulated building envelope might negatively affect the heating energy flexibility. Therefore, this paper explores the effectiveness of PCM in reducing heating costs by optimizing the PCM design and heating operation. The main objectives are to (i) quantify the performance of PCM in terms of overheating and cost savings, aligned with the recently introduced Time-of-Use (ToU) tariff and Spot pricing, (ii) identify the optimal design of PCM-incorporated building envelope, and (iii) apply a smart control with resetting temperature setpoints in-line with EN-ISO 52120-1 Standard. A multi-stage optimization process has been designed. It first identifies the optimal properties (i.e., melting temperature), position, and thickness of PCM. Secondly, it optimizes the profile of temperature setpoints, considering the seasonal thermal behavior of PCM. Co-simulation platform between IDA ICE 5.0 and Python 3.8 optimization engine is created for the heating control optimization. Results showed that PCM is more effective when incorporated into the interiors of external walls rather than in roofs. The optimal PCM design for Nordic buildings, featuring 75 mm-thick PCM with a melting temperature of 21 °C positioned within walls’ interior, has a varied impact on monthly energy savings across seasons, with a negative effect in spring and a significant positive effect in autumn. After the multi-stage optimization, the optimal scenario enables to achieve a maximum annual cost savings of 6.5 % with ToU tariff and 21.0 % with Spot pricing, while also eliminating overheating.

Comprehensive model for efficient microgrid operation: Addressing uncertainties and economic considerations

The fabrication of microgrids to harness renewable resources for local load provision has emerged as a promising concept. Efficient energy management and resource utilization within the electricity market have become crucial tasks for microgrid operation. This article presents a comprehensive model that addresses economic and technical considerations, as well as uncertainties associated with load, wind speed, and solar radiation. Specifically, the focus is on optimizing the utilization and energy consumption of controllable loads within a distributed energy system, taking into account consumer preferences and operational constraints. A two-stage planning problem is formulated to minimize microgrid operation costs and consumer payments, while considering load requirements, restrictions, and utility-imposed constraints. To solve this problem, an enhanced algorithm based on the mountain gazelle optimizer with improved local search operators is proposed, significantly enhancing its search capabilities. The retail electricity market is modeled using real-time pricing (RTP) and incentive-based regulation (IBR) tariffs to capture wholesale price fluctuations and discourage simultaneous consumption. Notably, price forecasting becomes essential in scheduling controllable loads, introducing uncertainties that are addressed by a two-point estimate method. Simulation results demonstrate the effectiveness and robustness of the proposed approach, exhibiting low standard deviation and high resilience. The results highlight the potential of the proposed method in achieving cost-effective and reliable operation of microgrids in the face of uncertainties and dynamic market conditions.

A pool-based electricity retail markets integrating renewable energy sources and electric vehicles in the presence of demand response program and conditional value at risk to enhance energy security

In restructured power systems, energy security has become a critical concern, driven by changes in laws and regulations and underscored by a defensive approach implemented by the independent system operator. This alignment highlights a strong interdependence between energy security, energy supply, and the economic aspects of energy management. The paper explores how consumers energy supply in restructured systems can be managed through Demand-Side Management (DSM) and Demand Response (DR) programs, considering economic efficiency and risk in scenarios that include Renewable Energy Sources (RES), suitable storage systems, and electric vehicles (EVs). An energy security index is introduced to evaluate the impact of DR programs on both electrical and thermal energy, considering the economic risks involved. A hybrid pool-based electricity retail market approach is utilized to optimize energy security, supply, and consumption. The significant roles of RES, storage systems, and EVs are highlighted in this context. Economic and environmental optimization is achieved using the Conditional Value at Risk (CVaR) method, facilitating strategic decision-making under varied risk conditions. The energy supply security is further analyzed in the presence of electricity market retailers, exploring different pricing strategies. A Mixed-Integer Programming (MIP) model, implemented in GAMS software with the CONOPT solver, is used to examine pricing methods such as Time-of-Use (TOU) and real-time pricing. Findings reveal that DR programs lead to substantial increases in retailer profit and energy supply security value, with real-time pricing showing a significant 23.5 % increase in energy supply security value and a 24.5 % increase in retailer profit compared to fixed tariff pricing.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Determinants of Indonesia's Wheat Imports

Indonesia's wheat imports have consistently increased each year, driven by the growing population and demand for various wheat derivative products. Consequently, Indonesia has become the world's largest wheat importer in 2021. This study aims to determine the determinant factors of Indonesia’s wheat imports during the 1990-2021 period. The data used in this study is time series data of 32 years from 1990 to 2021, which consists of annual data on the Indonesian wheat import volume, GNI, wheat consumption, rice consumption, exchange rate value, interest rate, domestic rice production, wheat price, rice price and wheat flour import tariff agreement. The data was processed using Eviews 9 with multiple linear regression models using the Ordinary Least Square (OLS) method. The regression results showed that GNI, wheat consumption, rice consumption and the exchange rate had positive impacts on the Indonesian wheat import volume during 1990-2021, while domestic rice production had negative impacts. On the other hand, interest rate, wheat price, rice price and wheat flour import tariff agreement have no significant effect on Indonesian wheat import volume.

The Economic Potential of Agrivoltaic Systems in Apple Cultivation—A Hungarian Case Study

Agrivoltaic systems (AVS) allow the simultaneous use of land—as a limited resource—for crop production and electricity generation. This paper introduces the development prospects of AVS in Hungary with insights into international trends. The most important part is a complex economic analysis and a unit cost analysis of a 38 MWp capacity AVS, considering the most typical basic data in electricity and apple production. The applied risk analysis is based on a Monte Carlo simulation, the distribution function, and probabilities. To introduce the economic facet of the competitiveness of AVS, a comparative analysis was carried out between AVS, ground-mounted photovoltaic (GM-PV) systems, and conventional apple production systems (ConAPS). In the most probable scenario, the AVS was financially attractive (NPV = 70 million EUR under 30 years). Our correlation analysis shows that feed-in tariff (FIT) price and the role of financing are considered the dominant economic factors. A favorable FIT price enhances the profitability of AVS; however, it makes GM-PV systems more profitable compared to AVS, so it negatively affects the competitiveness of AVS systems. AVS operations result in a more balanced unit cost of apples and of electricity compared to the independent operation of GM-PV systems and of ConAPS; in addition, it allows for land saving and more intensive land use.

Designing corporate mobility as a service – Decision support and perspectives

Corporate Mobility as a Service (CMaaS) integrates company-exclusive fleets and public mobility services, such as car sharing, bike sharing, or taxis, into one multimodal mobility system. As a result, companies may reduce their own vehicle fleet, their total mobility costs, as well as their emissions by shifting trips to alternative mobility modes. Against this background, we present a strategic-tactical decision support tool for corporate mobility managers to design a CMaaS system. We include strategic decisions on the fleet size and composition of company-exclusive fleets. The fleet composition addresses decisions on the inclusion of various mobility modes, e.g., cars and bikes, as well as on the choice of vehicle classes, e.g., regarding drive train technologies, and on how these vehicles are provided to the company, e.g., leased or owned vehicles. Further, we include tactical decisions on the choice of price tariffs offered by public mobility providers. Herein, our decision support tool accounts for structural requirements, such as the spatio-temporal corporate mobility demand, costs, upper bounds on the number of vehicles, technical vehicle restrictions, and the use of micromobility modes, like bikes and scooters. Finally, a comprehensive case study with ten mobility services and driving profiles of 144 companies enables us to draw general insights on the current and future potentials of CMaaS. Results promise average cost savings of 33% and a shift of 21% of trips to public mobility services.

Dual channel sales in supply chain: live streaming or traditional e-commerce

In recent years, live streaming is becoming a popular channel to sell products all over the world. Compared to traditional e-commerce channel, live streaming channel may not only bring consumers more shopping convenience, but also pose consumers more privacy concern. This paper considers a supply chain consisting of a manufacturer and an e-tailer who sells through dual channels (i.e., live streaming and traditional e-commerce) to explore how shopping convenience and privacy concern affect the optimal decisions. We build game models of two pricing (exogenous and endogenous) and two incentive contracts (wholesale price and two-part tariff). We find that the optimal promotion efforts are decreasing in shopping convenience while increasing in privacy concerns under the wholesale price contract, and independent of them under the two-part tariff contract when pricing is not a decision (such as iPhone); the optimal promotion efforts are increasing in shopping convenience while decreasing in privacy concern when pricing is a decision (such as the seasonal products). Whether the retail pricing is a decision or not, supply chain coordination can be achieved by the two-part tariff contract, but not through the wholesale price contract. Further, the two-part tariff contract is more favorable to the manufacturer if the exogenously given retail price is low, and the wholesale price contract is more favorable to the manufacturer if the retail price is high; the two-part tariff contract is always more beneficial for the supply chain than the wholesale price contract. Finally, we extend our analysis to relax a more realistic form with a variable effort elasticity and verify the robustness of the theoretical results.

Managing Industry 4.0 supply chains with innovative and traditional products: Contract cessation points and value of information

In supply chain management, how contract designs relate to the production system with traditional and Industry 4.0 innovative products (I4I products) is under-explored. In this paper, we fill this literature gap by studying a two-period model in which a manufacturer strategically chooses to incorporate an I4I product line into a traditional product line. In this context, we show that there exists a critical market potential threshold above which the sale of the I4I product is higher than that of the traditional product even when the former is sold at a higher price. For both wholesale price and linear two-part tariff contracts, we show that the contract cessation points and critical market potential threshold behave oppositely. We also find that the innovation level for the I4I product would be higher when the manufacturer’s expected valuation towards the retailer’s cost is higher than the actual one. We extend our base model to demonstrate that the innovation level of the I4I product is higher when the manufacturer uses a linear two-part tariff contract instead of a wholesale price contract. Finally, we uncover that the quantity-dependent innovation investment results in increased profitability and innovation level for the manufacturer provided that the innovation investment coefficient is sufficiently high.

OPTIMALISASI ALUR DISTRIBUSI: MEMPERLANCAR EFISIENSI MELALUI DAFTAR HARGA PENGIRIMAN DAN MODA TRANSPORTASI YANG STRATEGIS

PT XY, as a digital-based logistics company with integrated solutions, is striving to enhance the efficiency of its distribution workflow in the face of increasingly complex global business dynamics. Confronting a competitive market, PT XY considers the compilation of a shipping and truck pricing list as an essential foundation to improve competitiveness, expand market share, and build a reputation in the ever-changing market. This approach is not merely a pricing strategy but a necessity to remain relevant and effectively meet customer needs in a dynamic business era. The chosen strategy involves collaboration with external parties, especially logistics transportation vendors, to formulate effective pricing tariffs and select the appropriate mode of transportation. The process of tariff formation includes an analysis of destination routes, carried loads, and transportation modes, with a focus on logistics efficiency and the needs of the Indonesian market. This research is conducted using a qualitative method with the aim of finding solutions to the challenges faced by PT XY.

Energy Management Scheme for Optimizing Multiple Smart Homes Equipped with Electric Vehicles

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38% was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV.

Fully Parallel Algorithm for Energy Storage Capacity Planning Under Joint Capacity and Energy Markets

Energy storage (ES), with its flexible characteristics, has been gaining attention in recent years. The ES planning problem is highly significant to establishing better utilization of ES in power systems, but different market regulations impact the ES planning strategy. Thus, this paper proposes a novel ES capacity planning model under the joint capacity and energy markets, which aims to minimize the total cost for power consumers. The great challenge is that the ES planning model has a large number of time periods, which significantly increases the problem dimensionality. In order to alleviate the computational burden, a fully parallel algorithm is proposed to temporally decompose the original problem into a series of small sub-problems, which can be solved in parallel. Moreover, we find that the corresponding analytical solutions to the sub-problems remarkably accelerate the calculation speed while ensuring accurate results. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Energy storage (ES) has become more and more essential to guaranteeing power balance in power and energy systems by shifting peak loads to valley loads. However, investors may face challenges to ES capacity planning due to the lack of business models. To address this challenge, price tariffs should be carefully investigated. In the practical power system, the market price should consider both the energy price and capacity price for industries and big companies. In the energy market, investors can gain a profit by selling energy at the peak load (high price) and buying energy in the valley (low price). It should be noted that the energy market cannot recover the ES investment cost, but investors can, in fact, reduce their capacity cost since ES can reduce the peak load. Consequently, we have designed a new business model for ES planning under joint capacity and energy markets to analyze the profits via the two market regulations. The computational burden is another challenge for the proposed multi-period convex optimization model. The model must consider a long-term simulation, potentially containing thousands of time periods, which can be difficult to solve. In order to alleviate the computational burden resulting from the long-term market simulation, we further propose a fully parallel algorithm to solve the proposed business model for ES quickly. To sum up, the proposed model and method have been tested on a practical company with a practical price tariff in China to show their effectiveness.

Regional feed-in tariff mechanism for photovoltaic power generation in China considering tradable green certificate revenue

This study introduces a new regional feed-in tariff (FIT) pricing mechanism for solar photovoltaic (PV) energy in China, informed by real option (RO) theory and incorporating the increasing significance of tradable green certificate (TGC) policy revenues. The mechanism aims to balance government and consumer burdens with investor benefits. By factoring in additional income from green certificate sales, the study calculates the internal rate of return (IRR) to fall between 8 and 16% and uses a Monte Carlo simulation to determine optimal FITs for 30 Chinese provinces. These optimal FITs are then compared with current FITs, which are categorized by three types of solar resource regions, revealing that many current FITs are set within an inappropriate range and require adjustment. Additionally, the study observes minor differences in provincial FITs within six regional power grids, suggesting that setting FIT guide prices at the regional grid level is more practical than by solar resource regions. A sensitivity analysis examines the impact of green certificate prices, carbon prices, and technological advancements on the FIT for solar PV, comparing scenarios in a realistic environment versus a more favorable one. The results indicate that in a better environment—with higher TGC and carbon prices and encouraged corporate innovation—the FIT for solar PV is closer to the cost of coal-fired electricity, thus promoting grid parity. The study advocates for adjustments to the FIT mechanism, higher TGC and carbon prices, and the encouragement of innovation to enhance the competitiveness of solar PV energy in China’s energy market.

Demand Response Method for the Domestic PV system

This paper describes the deployment of the well-known Demand Response principle in a domestic PV inverter and spot price tariff environment to minimize power purchase costs. The optimization problem is defined as a pure linear program which is solved using a suitable solver. The results are verified using a dataset that was obtained by metering at a real storage site that was managed by an expert on each monitored day.