Peak Pricing Research Articles

A Portfolio Selection Strategy Based on the Peak Price Involving Randomness

A Portfolio Selection Strategy Based on the Peak Price Involving Randomness

Economic Analysis of Demand Response Incorporated Optimal Power Flow

Demand Response (DR) is one of the most cost-effective and unfailing techniques used by utilities for consumer load shifting. This research paper presents different DR programs in deregulated environments. The description and the classification of DR along with their potential benefits and associated cost components are presented. In addition, most DR measurement indices and their evaluation are also highlighted. Initially, the economic load model incorporated thermal, wind, and energy storage by considering the elasticity market price from its calculated locational marginal pricing (LMP). The various DR programs like direct load control, critical peak pricing, real-time pricing, time of use, and capacity market programs are considered during this study. The effect of demand response in electricity prices is highlighted using a simulated study on IEEE 30 bus system. Simulation is done by the Shuffled Frog Leap Algorithm (SFLA). Comprehensive performance comparison on voltage deviations, losses, and cost with and without considering DR is also presented in this paper.

A novel peak shaving approach to improving load flexibility of the Allam cycle by integrating cold energy storage

Allam cycle is a promising alternative cycle for power generation, characterized by high net electrical efficiency and near-zero emissions. For efficient and load flexible operation, a novel system integrating Allam cycle and cold energy storage system (CES-Allam) has been proposed. The compression train is employed to increase the pressure of CO2-rich working fluid for both Allam and refrigeration subsystems. The ice slurry is used as a cold energy storage carrier, which is further used to assist in liquefying carbon dioxide (CO2) at a lower evaporating pressure, and leads to higher electrical power output during peak load hours. The thermodynamic and economic models of this integrated system have been developed for comprehensive evaluation. The results show that the proposed integrated system is technically feasible for load flexible operation with a wider range of power generation regulation capacity (28.46%–105.97%) than that of the conventional Allam system (40%–100%). The round trip efficiency is 71.09% for the cold energy storage system, which is equivalent to the adiabatic compressed air energy storage system. The payback period is 4.15 years for the cold energy storage subsystem, which indicates that the CES-Allam system is economically feasible and competitive. Moreover, the sensitivity analyses of electricity prices and natural gas shows that the proposed CES-Allam system is more suitable for areas with higher natural gas prices and greater differences in peak and valley electricity prices. The present study is expected to provide a detailed reference and guidance for the use of cold energy storage to improve the load flexibility of the Allam system.

Modelling power prices in markets with high shares of renewable energies and storages—The Norwegian example

Though power markets evolved into high shares of renewable energies markets, typical optimisation models, designed for thermal electricity markets, lead to prices with low volatility when applied to the Norwegian market. The objective of this work is to adapt typical optimisation models, so that they can depict price structures in markets with high shares of renewables and flexibilities. Additionally, also the sensitivity of the prices to the smallest parameter variations of the influencing factors shall be highlighted. For this purpose, the key factors which are qualitatively examined in the literature are selected. These are implemented in a standard electricity market model and subsequently analysed quantitatively. It is demonstrated that adding uncertainty and storage restrictions is sufficient to adequately represent typical price structures in Norway. Furthermore, it is shown that price structures are highly dependent on the parameters. E.g., limiting the maximum availability of storage power plants to 63%–69% leads to significant price peaks, resulting in a maximum price increase of up to 160 €/MWh. This indicates that the influencing factors identified here should be taken into consideration when modelling future power systems, although great diligence is recommended during parameterisation of these factors.

Forecasting and Clustering of Cassava Price by Machine Learning (A study of Cassava prices in Thailand)

Forecasting and Clustering the price of cassava is essential for agriculture, but the difficult part of forecasting is price fluctuation, in which the fact of prices is going up and down and be changed monthly. The paper proposes to forecast the price of Cassava by machine learning. The process had been calculated by the price of Cassava from January 2005 to February 2022, which has been collected for 17 years by the Office of Agricultural Economics, Ministry of Agriculture, and Cooperatives. The research on forecasting found that the method of Support Vector Machine including using add-on feature with Garlic Price and Potato Price showing the Root Mean Squared Error (RMSE) with the lowest point as of 0.10. If comparing to the Conventional method with the equal database. The result shows that the proposed method demonstrates the value of the Mean Absolute Percentage Error (MAPE) as 3.35%, it displays more effectively as 0.61%. For the final process of clustering the price by analyzing with K-mean, the result came up with a peak pricing period in December of 14.08%. Subsequently, the agricultures would apply the research result to implement their planting plan for profit-making.

A charging plan generation method for EV clusters considering the operation of regional distribution network

The continuous development of large-scale electric vehicles (EVs) has provided abundant dispatchable resources for the power grid. However, the existing control methods are not enough to support the effective interaction of the EVs and the grid. This paper proposes a method for generating a charging plan of EV clusters considering the stable operation of the regional distribution network, which fully considers the load regulation requirements of the grid and the charging economy of the EV clusters. Combined with the improved Particle Swarm Optimization based on Genetic Algorithm, a day-ahead charging plan for multiple EV clusters can be figured out. It can be proved by the case analysis that, compared with the disordered charging behavior and the participation of demand response with peak and valley electricity price, the charging plan generated by the method proposed can reduce the comprehensive cost of EV charging while optimizing the operation parameters of the distribution network, and it can reduce the calculation dimension in the process of plan generation.

Paths and policy adjustments for improving carbon-market liquidity in China

Weak liquidity and low spot prices for carbon are critical problems facing China's carbon markets. This study aims to quantify the liquidity of China's carbon markets under different carbon-pricing strategies by analyzing a model which integrates linkages between the government, markets, and firms. It uses the decoupling theory to set the penalty scale, aiming to reduce the effects of carbon emissions on economic growth. The model permits the study of policy adjustments for certified emission reduction, traders' coverage, and free quotas — the benchmarks of quota allocation. The theoretical analysis indicates that the effect of policy on market liquidity is mainly reflected in the penalty scale and quota-allocation rules. Numerical simulations indicate that the issuance of new quotas accounts for 3.3% of total allocated quotas and raises carbon prices by about 45%. Further, the carbon price rises with an increase in free quotas and then rapidly drops after a peak price of 57 yuan/ton. Our findings indicate that adding free quotas in the short term and expanding trading volume in the middle and long term will improve market liquidity and raise carbon prices.

Energy optimization in smart urban buildings using bio-inspired ant colony optimization

In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results.

A multi-objective peak regulation transaction optimization and benefits coordination model for multi-sources coupling system considering flexible load response

Based on the intermittent output and inverse peak regulation characteristics of wind power, a multisource peak regulation transaction optimization model that considers the feasibility of combining thermal power, energy storage, and demand response for both power generation and consumption is proposed in this paper. First, a multisource peak regulation transaction cost model is established by considering flexible load participation in peak regulation and price- and incentive-based demand responses. Subsequently, with the objective of minimizing both the peak regulation cost and wind curtailment rate, a reduced half gradient membership function is selected to transform the multiobjective model. Finally, by analyzing the roles of the different subjects in multisource peak regulation transactions, a compensation mechanism based on the Shapley method is designed, and a local power grid in northeast China is chosen as the simulation object. The results show the following: (1) When the thermal power generators (TPGs) transit from regular peak regulation to deep peak regulation, the wind curtailment rate decreases by 5.10%, and the peak regulation cost increases by $ 0.793 × 106. This indicates that the peak regulation cost of the TPGs and the income of the on-grid wind power need to be balanced. (2) When the energy storage and the demand response are combined for peak regulation, both the peak load regulation cost and wind curtailment rate reach the optimal values, decreasing by $ 0.642 × 106 and 5.72%, respectively, showing cooperative optimization. However, the TPGs require a higher regulation cost, whereas the other subjects achieve incremental benefits. (3) The compensation mechanism assists each subject in obtaining incremental benefits depending on the contribution rate. With the strengthening of the peak regulation of the TPGs, more energy storage and demand response output are introduced to meet the urgent peak regulation requirement, which leads to increased regulation revenue and optimal dispatching. In summary, the proposed transaction and compensation mechanism of multisource peak regulation can be used to balance the peak regulation ability and contributions of different subjects and establish the optimal peak regulation scheme.

Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes.

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model.

Bitcoin: A life in crises.

In this study, we investigate the BTC price time-series (17 August 2010-27 June 2021) and show that the 2017 pricing episode is not unique. We describe at least ten new events, which occurred since 2010-2011 and span more than five orders of price magnitudes ($US 1 -$US 60k). We find that those events have a similar duration of approx. 50-100 days. Although we are not able to predict times of a price peak, we however succeed to approximate the BTC price evolution using a function that is similar to a Fibonacci sequence. Finally, we complete a comparison with other types of financial instruments (equities, currencies, gold) which suggests that BTC may be classified as an illiquid asset.

One more for the road: Reconsidering whether OECD gasoline income and price elasticities have changed over time

This paper determines whether gasoline income and price elasticities have changed. To do so, the paper considers a balanced, but particularly long (1960–2017) panel of 17 OECD countries. In addition, it employs two methods that vary to the extent that they allow for cross-sectional and time heterogeneity: rolling window, mean group regressions and time varying estimates. We find that the price elasticity increased in absolute terms during the energy crises (1973–1985), peaking thereafter and then becoming smaller. While income elasticities are not constant over time, they do not deviate much from time-invariant estimates. Similarly, price elasticities have been more or less stable for about the past two decades. This last finding suggests that the price peak of the 1970s-early 1980s had a permanent effect on demand that was not replicated during the more recent (i.e., centered around 2008), and similar in magnitude, price increase and fall.

Energy-efficient scheduling for an energy-intensive industry under punitive electricity price

As global warming and climate change become more and more serious, governments and scholars are paying more and more attention to these problems. Considering that consumption in manufacturing accounts for about one-half of the world's total energy consumption, the industrial sector is extremely important for preventing global warming. Facing with a multi-objective scheduling problem in the energy-intensive industries under punitive electricity price, a hybrid flow shop scheduling problem with onsite PV power generation and battery system is proposed. An improved multi-objective evolutionary algorithm based on decomposition is adopted, which integrates the low-carbon method with existing algorithms in three stages. First, an enhanced NEH heuristic algorithm is used to improve the quantity of initial solution in the initialization. Then the right-shift and speed scaling method are adapted to reduce the total energy consumption without affecting the maximum completion time. In the third stage, the distribution of renewable energy is performed, and the renewable energy is rescheduled based on the electricity range. The result of random instances and critical peak pricing verify the effectiveness of the proposed study.

Nudges and peak pricing: A common pool resource energy conservation experiment

Using a contextualised common pool resource framework, individual energy consumption choices are studied. Individuals are nudged towards the socially optimal level of consumption by the use of a happy (sad) face if they are underconsuming (overconsuming). A price is set to incentivise a second group to choose the level of consumption observed in the nudge treatment in order to quantify the nudge via an equivalent price. Across all 10 periods, consumption is significantly lower in treatment groups compared to control groups without nudges and prices. The price treatment leads to an average level of consumption above the Nash equilibrium. There are implications for policy makers as the nudge treatment performs as well, on average, as an equivalent price without the implied loss of welfare, and is understood and integrated into subjects’ decision making quicker than an equivalent price. However, there is a tendency for both the nudge and the price to reinforce existing consumption behaviour as those who overconsume continue to overconsume.

The role of transdisciplinarity for mineral economics and mineral resource management: coping with fallacies related to phosphorus in science and practice

Mineral economics is a genuine multidisciplinary field dealing with economic and policy matters related to the production, distribution, and consumption of mineral commodities. We discuss why the increasing complexity, ambiguity, ambivalence, and social contestation of subjects of mineral economics promote the participation of mineral economists in transdisciplinary processes. These processes relate (a) knowledge from targeted interdisciplinary processes and (b) mitigated discourses among different stakeholders to provide (c) a shared problem definition and to attain shared basic knowledge about problem transformation science and practice. We discuss known examples of misperceptions regarding minerals (phosphorus), such as an imminent scarcity threat, the incorrectly understood causations of the 2007/2008 price peak and present the phosphorus ore-grades increased by 3.2% between 1983 and 2013 fallacies (which is based on the Simpson’s paradox), and only few countries have mineable reserves fallacy. Here, we also illuminate motivations underlying several mineral economics–related misunderstandings. We argue that societally relevant questions require an honest mineral economics knowledge brokership. The example of the Global TraPs project, which targeted sustainable phosphorus management, is presented. Honest brokership to attain a clearinghouse function of science requires trust formation in society. We argue that this calls for increasing the understandability of relationships that are not well-understood, such as “if prices rise, so do stocks.” Wellmer and Becker-Platen’s feedback control cycle may be considered an example of how complex mineral economics can become and how challenging it is to be understandable to scientists from different disciplines and faculties as well as to practitioners whose knowledge may well be used to cope with the complexity of given problems. Thus, the present paper represents a plea for mutual learning between science and practice in order to understand the complex social and economic challenges of mineral resource dynamics.

Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources

In the era of aiming toward reaching a sustainable ecosystem, the primary focus is to curb the emissions generated by non-conventional resources. One way to achieve this goal is to find an alternative to traditional power plants (TPP) by integrating various distributed energy resources (DERs) via a Virtual Power Plant (VPP) in modern power systems. Apart from reducing emissions, a VPP enhances the monetary benefits to all its participants, including the DER owners, participants, and utility personnel. In this paper, the multi-objective optimal scheduling of the VPP problem considering multiple renewable energy resources has been solved using the multi-objective black widow optimization (MOBWO) algorithm. Renewable resources consist of solar PV modules, wind turbines, fuel cells, electric loads, heat-only units, and CHP units. The weighting factor method was adopted to handle the multi-objective optimal scheduling (MOOS) problem by simultaneously maximizing profit and minimizing emission while satisfying the related constraints. In this research, a peak valley power pricing strategy is introduced and the optimal scheduling of the VPP is attained by performing a multi-objective scheduling strategy (MOSS), which is day-ahead (on an hourly basis) and 15-min based (for a one-day profile), to observe the behavior of the anticipated system with a better constraint handling method. This algorithm is capable of dealing with a complex problem in a reduced computational time, ensuring the attainment of the considered objective functions. The numerical results obtained by the MOBWO algorithm after 100 independent trials were compared with the latest published work showing the effectiveness and suitability of the developed system.

Application of SARIMA Model on Forecasting Wholesale Prices of Food Commodities in Tanzania: A Case of Maize, Rice and Beans

This research used a time series model called the Seasonal Autoregressive Integrated Moving Average (SARIMA) technique to model and forecast wholesale prices of Tanzania`s key food crops, notably maize, rice, and beans. The SARIMA model was selected due to its ability of fitting data with seasonality. Monthly wholesale prices data of the three crops between February 2004 to October 2021 in Tanzania were retrieved from the website of the Bank of Tanzania (BoT), resulting in 213 observations on each crop. The data from February 2004 to October, 2020 were used to fit a SARIMA model and data of November 2020 to October 2021 were used to validate the model. The results show that SARIMA (0,1,2) (1,0,1)12, SARIMA (0,1,0) (1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 are the most suitable models for forecasting wholesale prices of maize, rice and beans respectively. The model’s accuracy was tested using Mean Absolute Percent Error (MAPE), and the results were found satisfactory. The results reveal that maize, rice, and beans will all have higher peak prices in February 2022, with TZS 54,083/=, TZS 167,258/=, and TZS 180,117.68/= per 100kg, respectively. Therefore, SARIMA (0,1,2)(1,0,1)12, SARIMA (0,1,0)(1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 models could serve as a useful tool for modelling and forecasting monthly wholesale prices of maize, rice and beans respectively in Tanzania.

Community energy storage operation via reinforcement learning with eligibility traces

The operation of a community energy storage system (CESS) is challenging due to the volatility of photovoltaic distributed generation, electricity consumption, and energy prices. Selecting the optimal CESS setpoints during the day is a sequential decision problem under uncertainty, which can be solved using dynamic learning methods. This paper proposes a reinforcement learning (RL) technique based on temporal difference learning with eligibility traces (ET). It aims to minimize the day-ahead energy costs while maintaining the technical limits at the grid coupling point. The performance of the RL is compared against an oracle based on a deterministic mixed-integer second-order constraint program (MISOCP). The use of ET boosts the RL agent learning rate for the CESS operation problem. The ET effectively assigns credit to the action sequences that bring the CESS to a high state of charge before the peak prices, reducing the training time. The case study shows that the proposed method learns to operate the CESS effectively and ten times faster than common RL algorithms applied to energy systems such as Tabular Q-learning and Fitted-Q. Also, the RL agent operates the CESS 94% near the optimal, reducing the energy costs for the end-user up to 12%.

Energy poverty in Sweden: Using flexibility capital to describe household vulnerability to rising energy prices

Energy poverty has been kept at bay in Sweden for the past decades owing to several beneficial circumstances. However, geopolitical pressure and an accelerating energy transition are changing the circumstances and exposing vulnerabilities to energy poverty in Sweden particularly connected to electricity price peaks. The circumstantial nature of the exposed risks for energy poverty motivates a conceptualisation of the concept in Sweden through the energy vulnerability framework. Also recognising that flexibility is an important ability to be able to dodge short-term price peaks, this paper combines the energy vulnerability literature with the concept of flexibility capital. Using national survey data from 2021, this study seeks to explore vulnerability to heating-related energy poverty in Swedish single-family housing by analysing factors influencing households' self-perceived ability to pay for heating as well as their self-perceived flexibility capital. Logistic regression models revealed that there are geographic as well as sociodemographic factors influencing the energy vulnerability experienced by Swedish households. Extending the understanding of energy poverty vulnerability beyond mere heating affordability provides a more nuanced understanding of the different types of risks that may emerge among households; for example, low affordability combined with low flexibility capital increases the risk for financial effects of energy poverty, whereas low affordability combined with high flexibility capital increases the risk for energy poverty affecting comfort, convenience and wellbeing. By integrating energy vulnerability with flexibility capital, this paper contributes to a more holistic understanding of challenges connected to a transitioning energy system in general and in the Swedish context in particular.

Analysis of Price and Incentive Based Demand Response programs on Unit Commitment using Particle Swarm Optimization

The increase in power demand by various infrastructural development activities and industrial automations in recent years have made a vital effect with respect to the load demand. To effectively manage the load demand, several Load Management (LM) techniques has been adopted in all energy policy decisions. In the de-regulated power system, the Demand Side Management (DSM) owing to its advantages at economic environments are regarded as remarkable choice and has been extended to incorporate Demand Response Programs (DRPs) in the load management techniques. In this paper, a responsive load economic model is developed. This model is based on the two factors such as price elasticity of demand and welfare function of customers. A Demand Response (DR) based Unit Commitment (DRUC) problem is studied to execute the economic analysis of DRPs. The main idea behind the DRUC is to maximize the benefit of both customers and utilities. The Particle Swarm Optimization (PSO) technique has been used to resolve the unit commitment problem with and without DRPs. The optimization outcomes are also compared with the conventional methods. To verify the efficacy of the DRUC model, the conventional ten-unit test system is considered. The simulated result shows that Critical Peak Pricing (CPP) and Direct Load Control (DLC) gives the best peak reduction and Time of Use (TOU) gives the best total cost reduction among all the DRPs and the other conventional methods.