Wholesale Market Prices Research Articles

Original Research Article Analyzing Price Behaviour and Constraints in Marketing of Small Onion in Tamil Nadu, India

This study examines market trends and seasonal patterns in small onion Markets in Tamil Nadu, while also investigating challenges faced by various stakeholders in the onion supply chain. The various analyses have made use of secondary data that has been gathered from different sources. The NHRDF office in Coimbatore, NHB website and AGMARKNET website are the sources of the monthly data on wholesale market prices and arrivals of small onions f markets of Dindigul, Chennai, Coimbatore and Idukki from 2014 to 2023. Primary data also obtained to fulfill the one of the research objectives by surveying the sample of respondents. Two districts were selected purposefully: Perambalur and Trichy districts, based on the top onion producing districts in Tamil Nadu. The sample size constitutes 180 respondents (80 farmers, 40 retailers, 20 wholesalers and 40 consumers) were randomly interviewed with help of pre structured interview schedule. Trend analysis revealed increasing price trends across all markets, with Idukki experiencing the highest rate of increase. Seasonal index analysis indicated that prices in Tamil Nadu markets are influenced by arrivals, with lower prices during peak arrival periods., while Idukki market in Kerala prices less influenced by arrivals. Farmers struggle primarily with high labor costs (78%) and high cost of fertilizers pesticides and machineries (68.7%), while consumers face problems with price fluctuations (68.8%) and quality concerns (68.6%), retailers face problems with lack of storage facilities (76.27%) and absence of facilities for grading (64.85%), and wholesalers struggle with financial risks (79.2%). The study's findings will aid policymakers to focus on improving market infrastructure, regulating intermediaries, and supporting farmers in managing costs. Leveraging existing labor schemes could help alleviate workforce shortages. Furthermore, customers would gain from initiatives to maintain price stability and raise quality standards.

Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

A Customer-Centric Distributed Data-Driven Stochastic Coordination Method for Residential PV and BESS

An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers' energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network.

Monitoring the Italian transposition of the EU regulation concerning renewable energy communities and the relevant policies for battery storage

This paper deals with the transposition of the European Union (EU) Directive concerning Renewable Energy Communities (RECs) into the Italian legislation. The methodology aims at identifying those national provisions which do not comply with the EU legislation. Consequently, long and onerous infringement procedures may ultimately limit the development of RECs. Moreover, the proposed methodology quantifies the economic value of national policies concerning the participation of storage assets in RECs. Three policy options are compared against the case without flexible assets. To do so, ad-hoc mixed-integer linear programming optimization problems are developed. The assessment is based on the formulation of three key performance indicators, concerning the revenues and costs of the REC. The main data sources in the work are the actual Italian Wholesale Market prices and the price incentives introduced by relevant Italian authorities to support the operation of the RECs. Results indicate an incorrect transposition of the fundamental rights of the EU law. These concern a significant alteration of the membership criteria for prospective RECs and the introduction of external third-parties, which may bypass fundamental requirements set out in the EU law. Numerical results call for a profound revision of the policy options for battery storage in RECs since the envisaged configurations introduce structural limits to the REC's revenues This work can support researchers and policy makers at both national and EU-level in developing policy options and techno-economic measures that foster the development of new RECs.

Transition risk: Investment signals in a decarbonising electricity system

Zero‑carbon electricity is a pre-requisite for decarbonisation of the wider economy and many scenarios envisage rapid expansion of renewables. IEA estimates a need to double the annual rate of investment to 2030. A rapid transition to low carbon electricity creates a risk to investors because projects being financed now will generate their revenue in a zero‑carbon electricity system for which there is no track record of price formation. Although the cost of renewable power has fallen greatly, future revenues from renewables are still affected by market price risks that are often outside the control of generators. If risks are difficult to quantify, they may be mis-priced, leading to inefficient premiums being added to the cost of capital, which could increase the overall cost of the transition. In this context many countries around the world provide support for low carbon energy, such as the UK Contracts for Difference. In this paper we investigate exposure to price risk caused by uncertainty over the mix of technologies used to achieve decarbonisation, which we term ‘transition risk’, and how this varies under different technology mixes, and for different policy regimes. We show that exposing investors to transition risk could increase the cost of delivering the renewables needed for a zero‑carbon electricity system by around 25% or £7bn/year compared to policy options that reduce exposure to wholesale market price risk. The paper concludes that as long as transition risks remain high, de-risking policy mechanisms can help to minimise the overall cost of achieving net-zero.

Market integration of chickpea crop: an evidence of India

PurposeThe purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for the chickpea legume in major chickpea markets in India.Design/methodology/approachIn this paper, the authors employ the Johansen co-integration test, Granger causality test, vector autoregression (VAR), and vector error correction model (VECM) to examine the integration of markets. The authors use monthly wholesale and retail price data of the chickpea crop from select markets in India spanning January 2003–December 2020.FindingsThe results of this study strongly confirm the co-integration and interdependency of the selected chickpea markets in India. However, the speed of adjustment of prices in the wholesale market is weakest in Bikaner, followed by Daryapur and Narsinghpur; it is relatively moderate in Gulbarga. In contrast, the speed of adjustment is negative for Bhopal and Delhi, weak for Nasik, and moderate for retail market prices in Bangalore. The results of the causality test show that the Narsinghpur, Daryapur, and Gulbarga markets are the most influential, with bidirectional relations in the case of wholesale market prices. Meanwhile, the Bangalore market is the most connected and effective retail market among the selected retail markets. It has bidirectional price transmission with two other markets, i.e. Bhopal and Nasik.Research limitations/implicationsThis paper calls for forthcoming studies to investigate the impact of external and internal factors, such as market infrastructure; government policy regarding self-reliant production; product physical characteristics; and rate of utilization indicating market integration. They should also focus on strengthening information technology for the regular flow of market information to help farmers increase their incomes.Originality/valueVery few studies have explored market efficiency and direction of causality using both linear and nonlinear techniques for wholesale and retail prices of chickpea in India.

Synthesizing the market clearing mechanism based on the national power grid using hybrid of deep learning and econometric models: Evidence from the Japan Electric Power Exchange (JEPX) market

In liberalized electricity markets, formulating the techno-economic functions of each market's participant restricts the resulting market clearing mechanism (MCM) into the market equilibrium. This study aims to synthesize the MCM based on a new perspective that correlates the nationwide energy supply and demand equilibrium with the wholesale market's prices. The nationwide energy capacities are collected from all the Japanese transmission and distribution utilities from 2016 to 2022. The demand curve is structured based on estimating the price elasticity of demand (PED) considering regional and hourly cross-sections using two-stage econometrics. The supply curve is structured based on the merit order effect of all existing energy production technologies. A long short-term-memory forecasting model is employed to capture the non-stationarity and -linearity of the prices. The estimated PED varies spatiotemporally from −0.017 to −0.25. From the synthesized MCMs, Kyushu region's price drop to nearly zero is justified by the 93.5% clean supply and the use of regional interconnections. Nationally, nuclear, hydroelectric, and hydro-storage contributions to the supply mix and the balancing market launching have mitigated the price spike of 2022. In addition, the planned share increase of baseload resources in 2030 drives the prices lower with higher stability versus the current electricity mix.

Regional revenues of solar and wind generation in Texas

Effective regulatory policies, ample renewable energy potential, and a favorable business climate have led to a boom in wind farms and solar energy projects in Texas. Using a large sample of 15-min data for the six-year period of 2016–2021, we analyze per MWh revenues from solar generation and wind generation in the renewable regions of the Electric Reliability Council of Texas (ERCOT). We find that average regional revenues from variable renewable energy (VRE) for the daytime hours of 07:00–19:00 exceed the average levelized price of recently signed solar and wind power purchase agreements (PPAs), although average regional revenues for wind generation for the nighttime hours of 19:00–07:00 were lower than recent PPA prices. Moreover, VRE's 15-min regional revenues move with ERCOT's cap on the real-time market's energy price offers, regulatory price adder, daily wholesale natural gas price, 15-min nuclear energy generation, 15-min system load, 15-min regional solar energy and 15-min regional wind energy. While continued VRE development may suppress wholesale market prices, continued growth in electricity demand and increasing natural gas prices provide offsetting effects. Nevertheless, new policies are under development to mitigate the adverse effects of further VRE development on grid reliability and generation investment incentive.

Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals.

Impact of Global Fuel Price Volatility on the Korean Electric Sector: Market Risk Estimation and Electricity Rate Cap

This paper investigates the challenges that the Korean electric sector has encountered in 2022 under a regulated framework different from market-based approaches. The Ukraine-Russia war caused chaotic circumstances in every aspect of the global economy, and the energy market was not an exception. Every nation has been struggling in 2022 to secure its energy needs due to the supply chain disruptions associated with the geopolitical issues, as well as to minimize the economic impact. As electricity is a derivative product from fuels, it has serious vulnerability to fuel price volatility, unless the nation is self-sufficient in energy supply with 100% renewable energy sources or equivalent alternatives. When an electric market is deregulated with open market competition, the market cost of goods is simply passed through to end customers. However, it may not be the same case for a regulated utility that is subject to the rules set by public agencies. Korea Electric Power Corporation (KEPCO), a Korean public utility, has made every effort to stay away from bankruptcy due to its deficit of KRW 3 trillion (U$ 24 billion) in 2022. The main reason for the deficit is the regulated electric rate cap that KEPCO must charge to retail customers for the wholesale power purchased at market rates from the Korea Power Exchange. The price disparity may be explained by the year-on-year change in October: the rate cap is up 22% as opposed to the wholesale market price spike of 89%. This paper analyzes price diversions or volatilities of eight comparable asset classes, from a DJIA stock to the Korean electricity prices, in a historical context (January 2018-August 2022), and applies current fuel market parameters to estimate the value-at-risk, or price change, in Korea electricity prices to understand the estimated divergence between the two prices.

Risk-based optimal decision-making by the retailer in a mixed local and wholesale market environment considering demand response solution

This paper proposes a comprehensive model to determine the retailer strategy for purchasing electrical power from the wholesale and/or local market in an active distribution network. The uncertainties associated with the load and distributed generation resources in the active distribution network, the wholesale market price and the behavior of the local market players, are all considered in the presented model. A retailer in the demand response program is employed as retailers’ ability to govern the risks. A risk-based decision-making scheme is provided in this paper which takes into account every instrument that is accessible for retailers along with their associated uncertainties. The major target of this paper is to maximize the retailer benefit concerning a tolerable risk. In order to model risks, the scenario theories are exploited and for solving the optimization problem, particle swarm optimization (PSO) has been utilized. The proposed scheme has been simulated on an actual network and the obtained results confirm the effectiveness and computability of this method.

Optimal operation of active distribution networks hosting hybrid hydrogen‐electricity refuelling stations considering water demand under a stochastic‐IGDT approach

AbstractThis paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system.

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

Demand Elasticity Estimation from the Japanese National Electricity Grid Considering Wholesale Market Price

Demand Elasticity Estimation from the Japanese National Electricity Grid Considering Wholesale Market Price

Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

PRICE TRANSMISSION OF MELONS (CUCUMIS MELO AND CITRULLUS LANATUS) IN MYANMAR – CHINA BORDER TRADE

Border trade is the main export channel for agricultural products from Myanmar (formerly Burma). With an increasing quantity and value of exports during the last few decades, melons have become one of the major crops in the national export strategy of Myanmar. In this paper, the word “melons” is used to represent both watermelon (Citrullus lanatus) and muskmelon (Cucumis melo) cultivars. No previous research exists on price transmission between these countries, especially for the horticultural produce market. This study aims to measure the magnitude and speed of spatial price adjustment in melons exported from Myanmar to China. Price transmission in the melon export market in Myanmar is analyzed by studying the lead-lag relationship among prices in the border market in Myanmar and the selected wholesale markets in Beijing, China, using a vector autoregressive model (VAR). Prices are transmitted quickly from Myanmar to the selected wholesale markets in Beijing, China. The wholesale market price in Beijing, China lags only one day behind the Myanmar price. In the Myanmar border market, the melon price is affected only by its own lagged price. The adjustment of melon prices in one day suggests that market information is getting passed up the market channel, and there does not appear to be a need for a policy to improve market efficiency.

The decarbonization transition and U.S. electricity markets: Impacts and innovations

AbstractSeveral factors in recent years have converged in the U.S. to spur a focused effort on decarbonizing the electricity sector. First, in response to the threat of climate change, policy at all levels of governance is increasingly promoting and incentivizing the deployment of zero carbon solutions, especially variable renewable energy (VRE) sources such as utility‐scale wind and solar PV, as well as distributed energy resources (DERs) such as rooftop solar PV and battery energy storage systems. Second, the costs of these technologies have declined to result in major increases in their market penetration. Finally, in response to greater access to cheaper, clean technology solutions, customers have become more engaged and proactive in their energy choices, both as a way to lower energy costs and to be more environmentally responsible. These de‐carbonization dynamics are impacting electricity markets—those where competition has been introduced (restructured markets), as well as those where vertically integrated utilities maintain a monopoly (regulated markets). For example, increasing penetration of VRE has influenced wholesale market prices, and many of the organized wholesale markets have implemented initiatives to add greater flexibility in their market operations to accommodate larger amounts of VRE. In regulated markets, policy makers and regulators in many states are assessing a variety of changes in the existing regulatory framework to adapt to more DERs. This overview identifies the impacts of more VREs and DERs on each market structure, and describes key adaptations and changes in each market to accommodate these de‐carbonization trends.This article is categorized under: Climate and Environment > Net Zero Planning and Decarbonization Energy and Power Systems > Energy Infrastructure Policy and Economics > Energy Transitions

A comprehensive and modular set of appliance operation MILP models for demand response optimization

Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf.

Keep it short: Exploring the impacts of configuration choices on the recent economics of solar-plus-battery and wind-plus-battery hybrid energy plants

Commercial interest in renewable-battery hybrid power plants connected to the bulk power system (“hybrids”) is rapidly growing in the United States and globally. Power system planners need to anticipate the operational behavior of hybrids in order to ensure overall system reliability and planning the transmission network. However, hybrids' operational characteristics depend on underlying developer design choices, which have not been systematically explored in the literature. We fill this literature gap by computing hybrid net values across a wide range of configuration choices to evaluate trends in their development and identify factors that may alter those trends. We use historical wholesale market power prices in the seven U.S. organized wholesale power markets from 2012 to 2019 to optimize hybrid plant revenue and compute associated costs, accounting for battery degradation effects. Configuration choices modeled include battery duration, battery power capacity, size of the grid interconnection capacity relative to the generator power capacity, the size of PV panels relative to the inverter capacity, and the way that batteries and generators are coupled. We find that battery duration and battery capacity have the largest impact on the net value of solar and wind hybrids, with the most attractive hybrids having a two-hour battery duration ($4–12/MWh increase in comparison to eight-hour configurations). We find that it is more attractive to set the interconnection capacity to accommodate simultaneous discharge of the generator and the battery, as opposed to limiting the interconnection capacity to the generator power rating, particularly for solar hybrids in the ERCOT and SPP markets ($8–11/MWh increase). We show that our analytical results align with current commercial trends, suggesting that our net value framework can be used to understand both recent hybrid development activity and near-future trends in configurations of hybrids.

Stochastic coordination of the wind and solar energy using energy storage system based on real-time pricing

In this paper, stochastic synchronization of the wind and solar energy using energy storage system based on real-time pricing in the day-ahead market along with taking advantage of the potential of demand response programming has been analyzed. Since renewable energies, loads and prices are uncertain, and planning is based on real-time pricing, the optimal biding proposition considers the wind power, solar system, and energy storage system. Uncertainty is addressed to solve the bidding strategy in a day-ahead market for optimal wind and PV power and optimal charging for energy storage. Batteries are the most promising device to compensate for the fluctuations of wind and photovoltaic power plants to mitigate their uncertainty. In general, using MILP is a suitable approach to address uncertainty as long as a linear formulation is acceptable for modeling either with continuous variables or integer ones. By setting some scenarios to formulate market prices, imbalance of energy, wind and solar system, the uncertainty problems could be easily solved by MILP solver. The model created enables the retailer to realize the potentials of the demand response program and exploit high technical and economic advantages. To ensure fair prices, a set of regulating constraints is considered for sales prices imposed by the regulation committees. A model is presented to optimize the electricity trading strategy in the electricity market, considering the uncertainty in the wholesale market price and the demand level. The retailer considered in this paper is a distribution company that is the owner and operator of the networks and operates under real-time pricing regulations. To model demand response, the elasticity coefficient is used. The proposed solution is implemented on a standard 144-bus sample network using a nonlinear integer programming method. The presented method results provide helpful and valuable information based on the optimal method proposed by the retailers considering the demand response program and real-time pricing system.