Demand Price Research Articles

Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning

Eco-friendly technologies for sustainable energy development require the efficient utilization of energy resources. Real-time pricing (RTP), also known as dynamic pricing, offers advantages over other pricing systems by enabling demand response (DR) actions. However, existing methods for determining and controlling DR have limitations in managing an increasing demand and predicting future pricing. This paper presents a novel approach to address the limitations of existing methods for determining and controlling demand response (DR) in the context of dynamic pricing systems for sustainable energy development. By leveraging actor–critic agent reinforcement learning (RL) techniques, a dynamic pricing DR model is proposed for efficient energy management. The model’s learning framework was trained using DR and real-time pricing data extracted from the Australian Energy Market Operator (AEMO) spanning a period of 17 years. The efficacy of the RL-based dynamic pricing approach was evaluated through two predicting cases: actual-predicted demand and actual-predicted price. Initially, long short-term memory (LSTM) models were employed to predict price and demand, and the results were subsequently enhanced using the deep RL model. Remarkably, the proposed approach achieved an impressive accuracy of 99% for every 30 min future price prediction. The results demonstrated the efficiency of the proposed RL-based model in accurately predicting both demand and price for effective energy management.

Surge pricing and consumer surplus in the ride-hailing market: Evidence from China

Surge pricing leads to efficient operations of ride-hailing services, but its implications on consumer welfare are difficult to estimate owing to endogeneity biases resulting from the simultaneity between passenger demand and surge pricing. We address this issue and present a comprehensive study on consumer surplus estimation in the ride-hailing market using multi-day data of around two hundred thousand daily ride-hailing trips in Nanjing, China. We estimate a relationship between surge level and demand to obtain the elasticity of passenger demand relative to the surge level. Potential endogeneity issues are addressed by applying the dynamic panel generalized method of moments estimation. The price elasticity estimates are then used to compute the consumer surplus. Our results provide three important insights. First, the magnitude of the surge elasticity of demand during peak hours is the lowest compared to other periods. Second, the magnitude of elasticity is higher in urban areas during the off-peak than in suburban areas. Third, the consumer surplus per kilometer is lower in urban areas due to longer trip lengths at higher surge levels than in suburban areas. The consumer surplus generated by a ride-hailing company per yuan (Y) ranges between Y0.47 and Y0.89. Our findings are relevant for both ride-hailing companies and policymakers. While ride-hailing companies can decide areas, time of the day, and the extent of surge pricing based on the spatiotemporal heterogeneity in price elasticity, government agencies can use consumer surplus as a yardstick to devise pricing regulations.

Cost of credit, mortgage demand and house prices

This paper studies the relationship between mortgage rates and house prices. We exploit a subsidized mortgage program that reduced the mortgage rates of state-owned banks in Turkey during the summer of 2020 as an exogenous shock to provide causal estimates of a decrease in the cost of credit on mortgage demand and house prices. The effects are estimated using a detailed dataset on all house sales with mortgages. We find that a 1 percentage point decrease in annual mortgage rates led to an increase in individual mortgage loans by 3.3% and an increase in house prices by 1.6%.

Client Classification and Dynamic Pricing of Resources in Cloud using Cloud Simulator

In cloud computing scenarios, the resources like hardware or software or applications can be accessed whenever a client needs. The client needs to get a subscription to avail service. The usages are monitored by service provider for each client over a network. When more number of client issues a request in cloud, scheduling the resources and pricing of taken resources from the cloud have a huge difference on the incomes. The job request from the user is scheduled in cloud is done, based on the need of the client (i.e) client classification algorithm and based on the demand pricing is fixed (i.e) dynamic pricing scheme. These algorithms solves income optimization problem by allocating resources under a given price and pricing of the resources are based on the allocation. The experiments were proposed with cloud simulation. Cloud simulations results are measured with the first come first serve, client classification and dynamic pricing model. The proposed algorithm shows the better resource allocation based on the pricing method.

Application of economic model optimization algorithm for international trade based on big data technology

Abstract The advent of the era of big data has posed greater challenges to the economic model of international trade and opportunities for international trade. In this paper, from the application of big data in international trade, we study the SVM-based big data clustering analysis method for international trade and propose to optimize the objective function in the iterative process by using the individual fitness function of the chicken flock optimization algorithm for the problem that SVM is easy to fall into the optimal local solution. Then, the steps of international trade big data analysis by constructing CSO-SVM are used to forecast international port throughput, international trade exports, and imports. The average absolute error rate of the trained CSO-SVM model for the prediction of international port throughput decreased to 6.19%, and the accuracy of the prediction of international trade export volume and export price reached 86.86% and 87.83%, respectively, and the accuracy of the prediction of international trade import demand and import price reached 86.71% and 87.07%, respectively. During the test, CSO-SVM has higher prediction performance than other models. The analysis based on big data can better quantify the challenges faced by international trade, optimize international logistics scheduling, and improve import and export planning.

Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications

With the rise in electricity, gas and oil prices and the persistently high levels of carbon emissions, there is an increasing demand for effective energy management in energy systems, including electrical grids. Recent literature exhibits large potential for optimizing the behavior of such systems towards energy performance, reducing peak loads and exploiting environmentally friendly ways for energy production. However, the primary challenge relies on the optimization of such systems, which introduces significant complexities since they present quite dynamic behavior. Such cyberphysical frameworks usually integrate multiple interconnected components such as power plants, transmission lines, distribution networks and various types of energy-storage systems, while the behavior of these components is affected by various external factors such as user individual requirements, weather conditions, energy demand and market prices. Consequently, traditional optimal control approaches—such as Rule-Based Control (RBC)—prove inadequate to deal with the diverse dynamics which define the behavior of such complicated frameworks. Moreover, even sophisticated techniques—such as Model Predictive Control (MPC)—showcase model-related limitations that hinder the applicability of an optimal control scheme. To this end, AI model-free techniques such as Reinforcement Learning (RL) offer a fruitful potential for embedding efficient optimal control in cases of energy systems. Recent studies present promising results in various fields of engineering, indicating that RL frameworks may prove the key element for delivering efficient optimal control in smart buildings, electric vehicle charging and smart grid applications. The current paper provides a comprehensive review of RL implementations in energy systems frameworks—such as Renewable Energy Sources (RESs), Building Energy-Management Systems (BEMSs) and Electric Vehicle Charging Stations (EVCSs)—illustrating the benefits and the opportunities of such approaches. The work examines more than 80 highly cited papers focusing on recent RL research applications—between 2015 and 2023—and analyzes the model-free RL potential as regards the energy systems’ control optimization in the future.

Consumption heterogeneity and monetary policy in an open economy

We incorporate two types of agents (Ricardian versus Keynesian) into a standard open economy macro model. We find that consumption heterogeneity has major implications for the impact of monetary policy shocks, the international transmission mechanism, and the design of optimal monetary policy. With sticky prices, the existence of Keynesian agents causes a spillover of shocks across countries, and leads to the interdependence of optimal monetary targeting rules. In the case of local currency pricing, consumer heterogeneity leads an optimal monetary policy to generate currency misalignment and deviations from the law of one price. Theoretically, there are ranges of household heterogeneity in which monetary policy becomes ineffective, but this depends sensitively on the interaction of aggregate demand and relative price effects.

Demand‐side price‐responsive flexibility and baseline estimation through end‐to‐end learning

AbstractTime‐varying electricity tariffs provide consumers the flexibility to adjust their consumption patterns in response to price variations to reduce the cost of electricity while at the same time contributing to grid operation. As more homes and buildings utilize time‐varying tariffs, utilities and regulators must seek ways to model demand‐side flexibilities to predict future demands and design new incentives. This paper proposes a novel end‐to‐end deep learning framework that simultaneously identifies demand baselines and the price‐response model from the net demand measurements and price signals. A gradient‐descent approach is then proposed that backpropagates the net demand forecast errors to update the weights of the price‐response model and the weights of the baseline demand forecast jointly. The effectiveness of the approach is demonstrated through computation experiments with synthetic demand response traces and a large‐scale real‐world DR dataset. The results show that the approach accurately identifies the DR model, even without prior knowledge about the baseline demand.

Can auctions increase competition in the pension funds market? The Chilean experience

This paper analyses the auction policy over enrollees’ monopoly rights introduced in the Chilean pension system. This policy was designed to promote competition in the pension fund market driven by private firms, after 30 years of operation. Since the Chilean pension fund system has inspired dozens of countries in the last forty years, the analysis of the design and performance of its relatively new auction mechanism is of worldwide interest. We present a theoretical and empirical model. Our theoretical model illustrates firms’ incentives to participate in the auction process. Our empirical analysis focuses on the effect of auctions on outcomes, such as fees, mark-ups, demand price elasticity, returns, and risk premiums. Despite the evidence shown for the positive benefits of the auction implementation, the current mechanism design is not considering that the biggest issue is the low individuals’ price response levels. Importantly, the current auction design only incentivizes new entrants to participate. Thus, the design generates low competition in the auction processes. Proper design should incentivize all firms to participate. Besides, we find that consumers’ price elasticity increased after the implementation of auctions, although demand is still generally inelastic. Interestingly, non-winning auction firms did not react in fees but may have reacted in other characteristics, such as returns and risk premiums.

A novel layered double hydroxides oleogel lubricant: Inspired by conventional greases

Grease lubricants serve as a simple, effective and convenient source of lubrication for saving energy. However, the most applicable lithium greases meet contradictory pressures of high performance and low pricing demands because of the substantial development of the global electric vehicles industry and the ubiquity of lithium batteries. In order to alleviate the dependence on lithium resources and develop resource-widespread and economical lubricating grease, herein, a class of thickener and oleogel system based on organic acid intercalated layered double hydroxides (LDHs) inspired by traditional grease has been reported. LDHs materials can be regarded as a preassembled “salt-like” compound, comparable to the self-assembled traditional metal soap salt thickener. LDHs oleogels exhibit viscoelastic properties and tribological performance similar to those of lithium greases. This study presents ideas for future development through layered materials regarding as "salt-like" compounds as a replacement for traditional grease thickeners.

Strategic logistics service procurement in shipping supply chains

This paper develops a game-theoretic model to explore the strategic procurement of logistics services in a shipping supply chain, where a freight forwarder canvasses orders and provides one-stop service to shippers. The forwarder faces a decision between in-house and outsourcing modes for inland logistics services, as well as the choice to purchase ocean shipping services through a forward agreement or in a spot market. The spot prices of inland logistics and ocean shipping services are positively correlated with shipper demand, meaning that changes in shipper demand can affect the spot prices. Our analysis indicates that the responsiveness of spot prices could be beneficial to the carrier but detrimental to the forwarder. Hence, the forwarder would adopt a forward agreement to purchase ocean shipping service even when the forward price is slightly higher than the expected spot price. Moreover, a forward agreement could be reached if the market size is relatively small. Finally, we show that the forwarder should adopt the in-house mode for inland logistics services, even when the in-house mode cost is higher than the expected outsourcing cost. This study contributes the literature by revealing how the correlation between shipper demand and spot prices for two types of logistics services affects the procurement decisions of a freight forwarder.

Performance of BARI-released gladiolus varieties under the climatic conditions of young Brahmaputra and Jamuna floodplain

The experiment was conducted in the farmers’ field at the Farming System Research and Development site (FSRD), Atia, Delduar, Tangail during the Rabi season of 2018-19 and 2019-20 to evaluate the performance of BARI released gladiolus varieties and popularize these varieties among the farmers of Tangail region. BARI developed three varieties of gladiolus, namely BARI Gladiolus-3, BARI Gladiolus-4, and BARI Gladiolus-5. Among the three varieties, BARI Gladiolus-4 performed the best in terms of the spike (191009 piece ha-1) and corm (254322 piece ha-1) production from the average of 2018-19 and 2019-20. Though BARI Gladiolus-4 performed the best in terms of yield, the maximum economic return was obtained from BARI Galdiolus-5 due to a higher spike in demand and market price compared to the other two varieties. The highest gross return (Tk.2406215 ha-1), gross margin (Tk. 1402546 ha-1), as well as BCR (2.40), were also recorded in BARI Gladiolus-5 followed by BARI Gladiolus-4. The lowest gross return (Tk. 1837608 ha-1), gross margin (Tk. 833939 ha-1) as well as BCR (1.83), were obtained from BARI Gladiolus-3. The farmers under the AEZ-8 areas can become economically benefitted by the production of BARI Gladiolus-5 and BARI Gladiolus-4. Bangladesh J. Agri. 2023, 48(1): 10-18

Price and income elasticities of industrial electricity demand in Brazil: estimates and implications of COVID-19

PurposeThis study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.Design/methodology/approachDynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.FindingsThe short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.Originality/valueThe research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

Correlations between component size green hydrogen demand and breakeven price for energy islands

The topic of energy islands is currently a focal point in the push for the energy transition. An ambitious project in the North Sea aims to build an offshore wind-powered electrolyser for green hydrogen production. Power-to-X (PtX) is a process of converting renewable electricity into hydrogen-based energy carriers, such as natural gas, liquid fuels and chemicals. PtH2 represents a subset of PtX, wherein hydrogen is the resultant green energy from the conversion process. Many uncertainties surround PtH2 plants, affecting the economic success of the investment and making the price of hydrogen and the levelized cost of hydrogen (LCOH) of this technology uncompetitive. Several studies have analysed PtH2 layouts to identify the hydrogen price without considering how component capacities and external inputs affect the breakeven price. Unlike previous works, this paper investigates component capacity dependencies under variables such as wind and hydrogen demand shape for dedicated/non-dedicated system layouts. To this end, the techno-economic analysis finds the breakeven price, optimising the components to reach the lowest selling price. Results show that the hydrogen price can reach 2.2 €/kg for a non-dedicated system for certain combinations of maximum demand and electrolyser capacity. Furthermore, the LCOH analysis revealed that the offshore wind electrolyser system is currently uncompetitive with hydrogen production from carbon-based technologies but is competitive with renewable technologies.The sensitivity analysis reveals the green electricity price in the non-dedicated case for which a dedicated system has a lower optimum hydrogen price. The price limit for the dedicated case is 116 €/MWh.

Economic, social and environmental spillovers decrease the benefits of a global dietary shift.

Dietary shifts are key for enhancing the sustainability of current food systems but need to account for potential economic, social and environmental indirect effects as well. By tracing physical quantities of biomass along supply chains in a global economic model, we investigate the benefits of adopting the EAT-Lancet diet and other social, economic and environmental spillovers in the wider economy. We find that decreased global food demand reduces global biomass production, food prices, trade, land use and food loss and waste but also reduces food affordability for low-income agricultural households. In sub-Saharan Africa, increased food demand and higher prices decrease food affordability also for non-agricultural households. Economic spillovers into non-food sectors limit agricultural land and greenhouse gas reductions as cheaper biomass is demanded more for non-food use. From an environmental perspective, economy-wide greenhouse gas emissions increase as lower global food demand at lower prices frees income subsequently spent on non-food items.

ANALYSIS OF THE BEHAVIOR OF BALANCE VALUES OF COSTS, QUALITY AND COMFORT OF MEDICAL SERVICES DEPENDING ON THE PATIENT'S MEDICAL EDUCATION

Objective of the study. To evaluate the state of surgical services for patients with uterine fibroids and/or ovarian cysts in hospitals in Vinnytsia by the correspondence of the balance values of costs, quality, and comfort of medical services depending on the patient's medical education.
 Materials and methods. The analysis, design, and program of the study are based on the econometric model of D. Dranov and M. Satterthwaite, which relates the balance values of costs, quality, and comfort of health care services to individualized elasticities of demand. The model is empirically specified for the purposes of the study in a proper structural form. On this theoretical basis, we studied the state of the provision of surgical services to patients with uterine fibroids and/or ovarian cysts by a combined (retrospective + prospective) study of the main array, namely, 150 medical records of inpatients of gynecological departments from three medical diagnostic institutions of Vinnytsia hospitalized over a six-year period.
 Results. The behavior of the balance values of costs, quality, and comfort of medical services depending on the patient's medical education was analyzed.
 Conclusions. The hypothesis of the existence of responsiveness, namely, a decrease in the individualized elasticity of demand for price, quality, and comfort due to lack of patient awareness leads to a reduction in the balance values of costs, quality, and comfort, has been previously confirmed. In our opinion, the insufficient reliability of the effects is due solely to the small amount of data. The presence of properly oriented shifts in balance sheet values indicates the organization of inpatient obstetric and gynecological care for the nosologies under consideration that is congruent with the market environment. At the same time, the organization of inpatient obstetric and gynecological care for the considered nosologies meets the conditions of optimization, on the basis of which a theoretical model and a system of structural dependencies were developed, and therefore, they offer marketable and cost-effective solutions.

Demand side management in a microgrid to reduce the peak consumption cost

Microgrid stands for the participation of renewable sources such as solar and wind in the existing energy system in order to increase the availability and reliability of energy for consumers and to make the atmosphere carbon-free by reducing dependence on the main grid. However, the non-linearities inherent in the nature of the microgrid and network components make the programming process complex. Therefore, the efficient but linear model for microgrid resource planning algorithms is gaining interest today due to its simplicity and computational speed. On the contrary, for demand-side management, reducing the peak demand price by using different methods such as load trimming and valley filling by varying the use of flexible loads on the consumer side, which is an easier option for microgrid operators, rush hour price for consumer satisfaction. This paper gives an idea of the above purpose by designing an average model of a solar microgrid with the implementation of the maximum power point tracking (MPPT) algorithm method for stability of the system frequency and a battery storage system with a controller. You then plan the microgrid using the demand side management (DSM) method in order to reduce the cause of the price spikes.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use (TOU) strategy can effectively improve the energy consumption mode of customers, reduce the peak-valley difference of load curve, and optimize the allocation of energy resources. This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use. First, this study proposes a period partitioning model, which is based on a moving boundary technique with constraint factors, and the Dunn Validity Index (DVI) is used as the objective to solve the period partitioning. Second, a control strategy for the curtailable flexible load is investigated, and a TOU strategy is utilized for further modifying load curve. Third, a price demand response strategy for adjusting transferable load is proposed in this paper. Finally, through the case study analysis of typical daily flexible load curve, the efficiency and correctness of the proposed method and model are validated and proved.

The Russian-Ukrainian War in 2022 (economic and political effects)


 The Russian-Ukrainian war that broke out in February 2022 led to global effects, especially in the economic and political field. In the economic field, it led to a significant rise in prices, especially in the energy markets. The reason for this rise in prices is the exacerbation of tensions on the Russian-Ukrainian border due to the presence of military build-ups. Because Russia reduced its gas supplies to Europe, before the outbreak of the military operation, which was met by the countries of the Western bloc with intense economic sanctions, most of the Russian sectors were targeted, except for the energy sector, because it is a vital element for European countries, and the high rates of global inflation negatively affect the world. This is due to the lack of supply and the lack of risk in it, which causes increased demand and high prices, and by reading the global economic reality, an important question is raised in this regard: Will the high global inflation rates remain a continuous or temporary crisis? The answer to such a question lies in all countries of the world developing quick economic solutions, such as removing taxes and fees on production inputs and local industries and providing support to them, as well as the agricultural and commercial sector. These proposals are more effective in developing countries because they are the most affected by the problem of high rates of inflation ، In the political sphere, the sanctions imposed on Russia have an economic aspect. But its essence is political, because every measure that disrupts the economic wheel is a direct and clear effect of politics. Relations with countries may worsen, and this creates political pressures that will influence the reversal of some decisions that create problems in international politics. A good example of that is the following table, which will clarify the nature of sanctions. imposed by some countries on Russia.
 

Region-wise evaluation of price-based demand response programs in Japan’s wholesale electricity market considering microeconomic equilibrium

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user's profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user's social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%-7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%-22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.