Tariff Market Research Articles

Основные предвестники формирования общепринятых практик осуществления мультимодальных перевозок

The article provides an overview of the history of the emergence of containers, within the framework of such a phenomenon as the container revolution, and also analyzes the modern potential of large transport and logistics infrastructure facilities, as a result and result of the container development trend. As part of the review of the development of modern trends in multimodal transportation, the issue of containerization is quite important, because today a container is not only an equipment for cargo transportation, but also an integral part of the supply chain, and is also able to significantly influence the formation of market tariffs for cargo transportation. Containerization of cargo transportation contributed to the formation of a modern transport and logistics paradigm in the context of international transportation, and also influenced the evolution of existing types of vehicles and special equipment, adapting them to mass transportation of metal containers. Over the history of its existence and development, containers have been adapted for the transportation of a wide variety of types of cargo from dangerous and temperature to bulk and bulk. In this article, within the framework of the calculation unit for assessing the potential of the modern global transport and logistics system, an analysis of the capacity of the largest marine infrastructure facilities - seaports is used, since it was with sea transportation that the containerization process began.

Market bidding method for the inter-provincial delivery of cascaded hydroelectric plants in day-ahead markets considering settlement rules

Chinese electricity market reform poses huge challenges to hydropower operations and electricity trading. This study proposes a scheduling method coupling priority electricity and day-ahead trading for large hydropower plants. The study focuses on complex factors such as tariff uncertainty, different types of electricity settlement rules, and inter-provincial electricity transmission links. Spot market tariff scenarios are determined through the Latin hypercube and the K-means methods. A performance formulation of priority electricity deviation considering settlement assessment rules is established. A transmission description for different sub-plants and a triangular linear interpolation method based on binary independent branching mode are proposed to solve inter-regional transmission connections and hydraulic coupling in cascaded hydropower plants, respectively. Finally, the Big M method is employed to equivalently transform the complex non-linear problem into a mixed-integer linear programming (MILP) model. The method is verified with the day-ahead operation of four large hydropower plants downstream of the Jinsha River in China as a case study. Settlement assessment rules, inter-regional power transmission, and price uncertainty are analyzed in three different cases. Three conclusions are obtained: 1) the priority electricity performance rate and the price are positively correlated, which is useful to guide hydropower plants to actively participate in the market. 2) Introducing the prediction error of electricity price in the model can help avoid market decision risk and improve the expected return by approximately 1.2%. 3) Considering the settlement penalty rule is helpful for power generation enterprises to improve power allocation and thus seek higher revenue compared to traditional methods without considering it.

Emissions and prices are anticorrelated in Australia’s electricity grid, undermining the potential of energy storage to support decarbonisation

Recent work has shown that energy storage operating in a CO2 intensive grid can increase greenhouse gas (GHG) emissions. In this paper we sought to characterise the emissions of Australia’s electricity grid to inform the planning and operation of energy storage with the goal of minimising emissions associated with energy storage and supporting the rapid decarbonisation of Australia’s energy system.To do so, a marginal emissions factor (MEF), representative of the emissions intensity of the marginal generator (the generator with the highest bid price) in each time period, was calculated for the Australian National Electricity Market (NEM) from 2013 to 2021. Through analysis, significant variation was discovered in the MEF’s intra-day variability, with high MEF values occurring overnight and low MEF values occurring during times of peak demand. Compared against the average 30-min spot price across the day, a strong anti-correlation was calculated between the MEF and the spot price.Using these results, the importance of energy storage operated to minimise both costs and emissions was highlighted. By taking the key finding of its anti-correlation with price, the MEF can be simply implemented in the real world, including through dynamic carbon incentives and market tariffs, to ensure emissions are being reduced both in the short and long-term.

The incidence of foreign market tariffs on farmland rental rates

We estimate the impact of tariffs faced by US agricultural exports on farmland rents. The localized tariff is determined by the average of tariffs across trading partners for the crops produced within the county. We utilize shift-share designs to avoid endogeneity concerns that arise because factors affecting rents could also affect trade flows and cropping patterns. Using the county-level data from 2008 to 2017, we find that a one percentage point decrease in the localized tariff increases rents by 3%–6%. The 2018 Chinese retaliatory tariffs would have decreased rents by about 3% in the absence of any government support.

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

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

Dynamic retail market tariff design for an electricity aggregator using reinforcement learning

The role of retailers, as energy providers for end-users, in restructured retail electricity markets becomes substantial. The increasing share of distributed energy resources and electrification in different sectors bring several challenges to retailers. Among these challenges, procuring electricity and maintaining system reliability during peak times induce high costs to retailers. Therefore, they need to accurately predict customers’ demand to participate in the wholesale market and develop proper tariff mechanisms considering other retailers’ behavior to maximize their profit. This paper develops the design of an autonomous retailer in which a Sequence-to-Sequence (Seq2Seq) algorithm is employed to predict customers’ net demand. Furthermore, using Reinforcement Learning (RL), the proposed retailer designs tariff mechanisms based on other retailers’ behavior and customers’ load profiles. The proposed design of the retailer is evaluated on a retail market simulation platform called Power TAC, in which autonomous retailers compete in retail, wholesale, and balancing markets to maximize their profits. The results show the accuracy of the proposed load prediction method compared with other methods and successful profit growth with a drop in fixed costs and balancing costs.

Medium-Term Hourly Electricity Tariff Forecasting Using Ensemble Models

Forecasting electricity tariff rates is necessary for large suppliers, consumers, and power brokers working in the wholesale markets. Meanwhile, tariff rates of the retail market are also hourly changed for certain groups of electricity consumers. It creates more efficient electrical load regulation opportunities than the traditional load leveling approach. Power facilities that include controlled load consumers or local generation can use their capabilities by adjusting the load curve according to tariff rates. This work aims to study the potential for medium-term forecasting of retail electricity tariff rates and develop a predictive machine learning model. Hourly data on the retail market tariffs of the Novosibirsk region (Siberia) for four years were collected, several machine learning models were applied, and an analysis of the input parameters for forecasting was carried out. The most significant results are the proof of the possibility of obtaining the month ahead electricity tariff rate forecast with the mean absolute percentage error 4 %. It could be used for electricity costs reduction by regulating the load curve. It was shown that the discrete models based on ensembles of logical rules give higher accuracy than models based on continuous and piecewise continuous functions, such as neural networks. The significance of the obtained results is the proposed approach for month ahead electricity tariff rates forecasting, which was verified on a four-year dataset with an error of 4 %. The approach is based on open data and open-source machine learning models, which allow specialists with even a basic level of data science skills to put it into practice.

Hydrogen energy storage systems to improve wind power plant efficiency considering electricity tariff dynamics

One of the limitations of the efficiency of renewable energy sources is the stochastic nature of generation; consequently, it is necessary to use high-capacity energy storage systems such as hydrogen storage for its integration into existing power networks. At the same time, electricity market tariffs for large enterprises change during the day. Therefore, it can be assumed that storing energy during cheaper hours and usage in more expensive hours allows increasing the efficiency of renewable energy sources. Evaluation of the economic efficiency of an energy storage system requires simulation with a step of at least 1 h for several years since the use of averaged production volume and averaged electricity tariffs will not allow obtaining an adequate to the task accuracy. A simulation model and software have been implemented to perform simulations and calculate the economic efficiency of a wind turbine with and without a hydrogen storage device. The methodology has been approved on three-year real data of wind speeds and electricity tariffs in the Novosibirsk region and Krasnodar Territory (Russian Federation).

Effects of Hydrogen Storage System and Renewable Energy Sources for Optimal Bidding Strategy in Electricity Market

This work suggested a novel model for obtaining optimum bidding/offering strategy to improve the benefits in case of big users. Aiming this regard, several electrical energy resources including: micro turbines, green power sources (wind turbine and photovoltaic system), power storage unit such as Hydrogen storage system with fuel cell, as well as mutual treaties are taken into account in offered model. Considering various models for uncertain parameters based on their natures such as power demand, electricity market tariffs, solar irradiation, temperature and wind speed is one of the contributions of the proposed model. Uncertainty of power demand is modeled by robust optimal method whereas remain uncertain parameters are incorporated in model by stochastic method. Considering of wind speed cased is made by Weibull distribution. While, normal distribution is utilized for production of cases for electricity market tariffs, solar irradiation and temperature. In order to reduce the bidding error loss, the storage devices are corporate with green energy in power unbalanced conditions. Combined-integer linear programming method is applied for handling of pricing method profiles that have strength against considered uncertainties on power demand of big user. The obtained results confirm, the entire electrical energy supplement expenditure of big user in absence of demand uncertainty is $39,63 whereas it is augmented up to $49.47 to achieve robustness vs. demand uncertainty. Also, using of hydrogen storage system by considering the reliability index is reduced the bidding price of the system.

ОЦЕНКА ЭФФЕКТИВНОСТИ ВЫВОДА СОВРЕМЕННЫХ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ НА ОПТОВЫЙ РЫНОК ЭЛЕКТРОЭНЕРГИИ (МОЩНОСТИ) В УСЛОВИЯХ ВЛИЯНИЯ РЕГИОНАЛЬНОГО ЦЕНОВОГО АСПЕКТА

One of the key ways to reduce the cost of purchasing electricity for industrial consumers when entering the wholesale electricity market is to eliminate the payment of sales surcharges of regional guaranteeing suppliers. The difference in the values of sales surcharges in the regions of Russia, the irregular share of sales surcharges in the value of the final price of electricity, the constant dynamics of the growth of sales surcharges determine the need for a comparative analysis of the economic effect of entering the wholesale electricity market of industrial enterprises in different regions of Russia. On the basis of the author's indicator “average sales allowance”, a comparative assessment of the absolute values of sales allowances operating in different regions of Russia is carried out, which allows us to emphasize the significant differences in the economic effect obtained by industrial enterprises when entering the wholesale electricity (capacity) market, the value of which can reach sevenfold. Based on the analysis of the indicators of the share of the sales premium in the final price for the supply of electricity in the regions of the Volga Federal District, the conclusion is made: the value of the market tariff does not directly depend on the values of regional sales allowances, however, the share of the component of the sales premium in some regions exceeds 10%, which emphasizes the difference in the possibilities of obtaining an economic effect from work in the wholesale market for industrial enterprises. The article evaluates the dynamics of changes in the indicators of average sales surcharges for the subjects of the Russian Federation, which made it possible to identify the trend of growth of sales surcharges in most regions, that indicates the effectiveness of industrial enterprises entering the wholesale electricity market in the long term.

Entry-Proofness and Discriminatory Pricing under Adverse Selection

This paper studies competitive allocations under adverse selection. We first provide a general necessary and sufficient condition for entry on an inactive market to be unprofitable. We then use this result to characterize, for an active market, a unique budget-balanced allocation implemented by a market tariff making additional trades with an entrant unprofitable. Motivated by the recursive structure of this allocation, we finally show that it emerges as the essentially unique equilibrium outcome of a discriminatory ascending auction. These results yield sharp predictions for competitive nonexclusive markets. (JEL D11, D43, D82, D86)

Formulating best practice recommendations for prepaid electricity meter deployment in Soweto, South Africa – Capitalising on the developed‐world's experiences

This study seeks to formulate best practice indicators characterising the prepaid electricity meter market. This was undertaken by reviewing both developed and developing countries' experiences with the technology, conducting in‐depth interviews with key stakeholders, and applying the strength weaknesses opportunities threats analysis tool. The generated outcomes are used to formulate a set of best practice recommendations that can inform decisions on improving the acceptability rate of the technology in Soweto. We found that the introduction of the technology faces a number of challenges, broadly being its complete rejection by some communities. In particular, the inadequacy or absence of incentives, the absence of prepaid meter market tariffs and regulatory mechanisms are fundamental factors affecting acceptance levels and effectiveness of the technology in developing countries. The case of Soweto is unique in that it has a historical political basis that even led to some violent protests. To improve the acceptance level and increase the buy‐in of local communities in Soweto, we formulated a number of best practice recommendations including the need to factor in the programme design the historical context‐specificity of Soweto, its low‐income status, the need to put the communities at the centre of the programme and the implementation of incentive measures inspired from the developed world.

Economic analysis of two giant land snail marketing in Ibadan, Oyo State, Nigeria

Abstract The study examined the marketing and uses of two species of Giant African Land Snails (Archachatina marginata and Achatina achatina) in four (4) different market locations in Ibadan Oyo state, Nigeria. The data were collected using a structured and validated questionnaire administered interpersonally to 160 snail marketers in four randomly selected markets in selected Local Government Area (LGA) situated in Ibadan. Data collected were analysed using descriptive and inferential statistical tools; these include frequencies, mean, median, mode and multiple regression. Findings from the socioeconomic characteristics revealed that majority (98.1 %) of the respondents were female and only 1.9 % were male. Majority (91.9 %) of marketers were married with 27.5 % in the 31–40 years age group. Majority (89.4 %) raised their capital through personal savings and had over 16 years of marketing experience. All respondents (100 %) were involved in wholesale marketing of different species of snail whereas 65 % of them were into both wholesale and retail marketing. Also, 88.8 % were engaged in marketing of both species only. Most (70 %) of the respondents’ generated income per year was between ₦10,000–₦49,999 ($26–$130). All marketers ascertained that the major purpose of snail is for consumption. Also, the budgetary analysis (costs and returns) revealed that marketing of Archachatina marginata and Achatina achatina is a profitable business among the marketers with a high rate of return on investment. The most efficient market for both snail species was Sango market with efficiency values 1.77 and 1.82 for A. achatina and A. marginata, respectively. The coefficient of multiple determination, R2 value of 0.689 indicated that 68.9 % of the variation in the marketing price of snail is explained by transportation cost, cost price of snail and market tariff. The marketing efficiency was greater than one in all markets which showed that the markets were efficient in the marketing of Archachatina marginata and Achatina achatina. The study concluded that snail marketing is profitable given the market efficiencies and rate of returns on investment. Policy recommendation towards reduction in cost of transportation and market tariff is suggested as this would bring about improved market efficiencies and more returns.

Application of robust information game decision theory for risk-assessment problem in large scale users

This work suggested a novel method in order to solve uncertain problem with uncertainty on pool market tariff with respect to wind and photovoltaic generations as well as storage units. Aiming this regard, information gap decision theory method is applied for solving the considered problem. This way, 3 major models are considered into account for big industrial consumers including: risk-taker, risk-averse, and risk-neutral models. Efficiency of suggested methods are studied by exertion of proposed method on big consumer risk via various pool yield tariffs. In addition, appraised obtained results prove the efficiency of demand response program for decreasing the over expenditure of big consumer. Therefore, good impacts of demand response program caused decrease in big user expenditure.

Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China

The reused batteries have become a practical alternative to household energy storage system, which is conducive to the effective utilization of excessive roof photovoltaic power generation and the sustainable development of energy. Economic incentives are the driving force for residential consumers to develop photovoltaic and energy storage. This study combines a solar-load uncertainty model and economic analysis to assess the financial impact of adding a reused-battery energy storage system to a photovoltaic assemblage in the context of multi-tariff policies and photovoltaic resource regions in China. First, we classify the types of residents based on the correlation between the users’ electricity consumption behavior and solar radiation. Secondly, to characterize the solar-load uncertainty, a deep scenario generation method based on an improved variational autoencoder is proposed to generate solar-load scenarios. Then, a mixed-integer linear programming model is developed which takes solar-load uncertainty into account. Finally, the operating cost of photovoltaic with a reused-battery energy storage system for each type of residential user under multi-tariff policies in China considering solar load uncertainty is obtained. The results demonstrate that the generated scenarios can effectively describe the uncertainty of the photovoltaic output and residential load. And, the correlation between the users’ electricity consumption behavior and solar radiation can guide the residential customer to install the reused-battery energy storage system. Moreover, economic feasibility and sustainable development of photovoltaic with reused-battery energy storage system depending on the regulation of market tariff policy.

RETRACTED ARTICLE: Robust Optimization-Based Energy Pricing and Dispatching Model Using DSM for Smart Grid Aggregators to Tackle Price Uncertainty

In this paper, we have proposed a two-level, dual-stage demand side management technique to tackle the energy procurement tariff and power dispatch constraints confronted by a retailer who acts as an aggregator agent in smart grid to eliminate the communication gap between consumers and wholesale energy market. In primal stage the consumers’ demand response has been characterized by Stackelberg game in accordance with the retail tariff; therefore, the primal stage has two additional stages. A risk-aversive dispatched power secretarial to anticipate the market tariff uncertainties has been modeled by using the linear robust optimization and contains objective uncertainties in the secondary stage. The proposed algorithm is converted into a joint integral linear program by conjointly employing the duality approach. In addition, we have designed a heuristic technique to choose the parameters within the disjunctive problems depending on the elucidation of Lagrange multiplier. Moreover we have also proposed a solution for additional linear programming model to attain a probable enhanced bidding technique which assures the Pareto efficiency in the retailer’s profits over the entire uncertain period. The intense performance evaluation by different case studies shows the effectiveness of the proposed mathematical model.

Environmental and Economic Operation of Wind-PV-CCHP-Based Energy System Considering Risk Analysis Via Downside Risk Constraints Technique

Combined Cooling, Heating, and Electrical (CCHP)-based microgrids meet various energy demands at the same time. Risk analysis of such CCHP-based microgrids under environmental and economic constraints is a challenging issue. Furthermore, high penetration of various renewable energy sources in such CCHP-based microgrids clouds the issue. Thus, Downside risk constraints technique (DRCT) is employed in this paper to manage and minimize the risk associated with various uncertainties in the studied sample CCHP-based microgrid under environmental and economic constraints. The uncertainties of solar radiation, wind speed, market tariff and cooling, heating, and electrical demands are considered, and the stochastic optimization approach is used to model the mentioned uncertainties. The effect of the Real-Time Pricing rate of the Demand Response Program (RTP-DRP) has been studied in this paper. By analyzing the obtained results, it can be found that with considering DRCT, the risk-in-cost (RC) level can plunge to 0 respectively by paying 2.61% and 1.68% without and with RTP-DRP.

VidyutVanika: A Reinforcement Learning Based Broker Agent for a Power Trading Competition

A smart grid is an efficient and sustainable energy system that integrates diverse generation entities, distributed storage capacity, and smart appliances and buildings. A smart grid brings new kinds of participants in the energy market served by it, whose effect on the grid can only be determined through high fidelity simulations. Power TAC offers one such simulation platform using real-world weather data and complex state-of-the-art customer models. In Power TAC, autonomous energy brokers compete to make profits across tariff, wholesale and balancing markets while maintaining the stability of the grid. In this paper, we design an autonomous broker VidyutVanika, the runner-up in the 2018 Power TAC competition. VidyutVanika relies on reinforcement learning (RL) in the tariff market and dynamic programming in the wholesale market to solve modified versions of known Markov Decision Process (MDP) formulations in the respective markets. The novelty lies in defining the reward functions for MDPs, solving these MDPs, and the application of these solutions to real actions in the market. Unlike previous participating agents, VidyutVanika uses a neural network to predict the energy consumption of various customers using weather data. We use several heuristic ideas to bridge the gap between the restricted action spaces of the MDPs and the much more extensive action space available to VidyutVanika. These heuristics allow VidyutVanika to convert near-optimal fixed tariffs to time-of-use tariffs aimed at mitigating transmission capacity fees, spread out its orders across several auctions in the wholesale market to procure energy at a lower price, more accurately estimate parameters required for implementing the MDP solution in the wholesale market, and account for wholesale procurement costs while optimizing tariffs. We use Power TAC 2018 tournament data and controlled experiments to analyze the performance of VidyutVanika, and illustrate the efficacy of the above strategies.

A robust optimization method for bidding strategy by considering the compressed air energy storage

Power market tariff is a main problem in electricity yields that is challenged by yield markers. For tackling this challenge, optimization of offering/bidding methods is so crucial. Present work suggested a novel method called synthetic stochastic-robust approach for maximization of anticipated revenue of compressed air energy system (CAES), in which, uncertain parameters such as market tariff and max potential of cavern are taken into account. In this proposed method, modeling of market tariff uncertainty is done by means of a collection of cases through stochastic optimization whereas uncertainty of max potential of cavern is modeled by robust method. As well, this paper suggested a new method for achieving offering and bidding profiles that have robustness versus uncertain parameters including: market price and cavern max capacity. Achieved results confirm, entire benefit in absence of cavern uncertainty. In the other words, having robustness versus uncertainty of cavern max potential decreases entire revenue.

Assessment of a Kalina cycle for waste heat recovery in the cement industry

The evaluation of a Kalina cycle that is presented in the sequence aimed to estimate the potential of electricity generation from the exhaust gases of the cyclone preheater of the rotary kiln of a Brazilian cement factory with a daily production capacity of 2.1 thousand tons clinker. The thermodynamic, heat transfer, cost estimation, and optimization models we developed in the EES to estimate the dimensioning of the cycle pieces of equipment and to evaluate the Kalina cycle performance doing its optimization regarding net power, thermal and exergetic efficiency, and specific electricity generation cost. It observed that the increasing the ammonia concentration at the outlet of the evaporator and reducing the pinch point in the evaporator leads to the increase in net power, while the increasing in the turbine inlet pressure decrease the cost of the electricity generated. The heat recovery boiler and the condenser are decisive (together with the turbine) in estimating the investment cost of the cogeneration plant for waste heat recovery in the cement industry. With slightly higher cost in the specific electricity generation, the maximizing power generation is the appropriate objective function for the optimization of the studied Kalina cycle. The values of power generation, 2429.056 kW, thermal efficiency (0.233) and exergetic (0.478) are also very promising. In the Brazilian tariff market, the cost values obtained (∼0.2847 R$/kWh and 5507 R$/kW) are competitive in the scenario of the Brazilian electricity sector in the second decade of the 21st century.