Peak Pricing Research Articles

Fractional order PID controllers for collaborative energy management in IoT-Smart cities: Hybrid optimization algorithms for demand

Modern energy markets have undergone a revolution due to the development of smart grids, which allow users to take part in demand response (DR) initiatives and keep the balance between demand and power generation. This work presents a new method with a Fractional Order PID controller (FOPID) for optimizing the power consumption of appliances in smart homes. An improved Secretary Bird Optimization (ISBO) algorithm is presented in this research for better scheduling appliance power consumption under time-of-use (TOU) and critical peak pricing (CPP) schemes. This strategy uses TOU and CPP, two dynamic pricing schemes, to minimize peak demand and cut energy expenses. The proposed strategy creates an energy management system (EMS) that is more effective by integrating energy storage devices (ESD) and renewable energy sources (RES) with the electrical grid. Due to this integration with FOPID, the best possible scheduling of appliances is made possible by CPP and TOU pricing schemes, which are intended to shift load and lower the peak-to-average ratio (PAR). The FOPID had been incorporated with EMS to enhance the stability as well as precision. Reductions in energy consumption and electricity costs across a range of scenarios are evident in the simulation results conducted in the MATLAB environment.

Adaptive peak price with lazy updates for short-term portfolio optimization

This paper introduces the novel Adaptive Peak Price with Lazy Updates (APPLU) approach for short-term portfolio optimization (SPO), a method that innovatively combines the Radial Basis Function (RBF) and a new lazy update approach to address the unique challenges of SPO. Our approach is tailored to balance the dual objectives of maximizing returns and minimizing transaction costs, which are critical concerns in dynamically allocating wealth among various assets over time. Unlike conventional methods that primarily rely directly on peak price information, APPLU introduces an adaptive peak price using RBFs to capture continuous depreciation information of assets, thereby alleviating the aggressive nature of traditional peak price strategies and avoiding frequent trades. Furthermore, we propose a new lazy update approach that employs unsquared l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l^{2}$$\\end{document}-norm regularization to represent portfolio changes. This approach contrasts with squared l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l^{2}$$\\end{document}-norm regularization, which disproportionately penalizes larger portfolio changes while being more lenient towards smaller changes. Thereby, our methodology offers a more balanced and effective approach to portfolio adjustment. Extensive experiments conducted on seven real datasets demonstrate that APPLU outperforms existing strategies in terms of cumulative return and risk-adjusted return, while effectively controlling transaction costs and maintaining a moderate wealth accumulation strategy.

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Renewable Energy Communities (REC) can largely contribute to building decarbonization targets and provide flexibility through the adoption of advanced control strategies of the energy systems. This work investigates how the role of flexibility sources will be impacted by shifting towards advanced control strategies under a high penetration of variable Renewable Energy Sources, in the following years. A large residential area with diverse energy systems, building envelope configurations, and energy demand patterns is modeled with the simulation environment RECsim, a virtual testbed for the implementation of energy management strategies in REC. Photovoltaic (PV) panels, Battery Energy Storage and Thermal Energy Storage (TES) of different sizes for each household provide a realistic description of a REC which includes both consumers and prosumers.This study explores a scenario in which advanced controllers based on Deep Reinforcement Learning (DRL) replace existing Rule-Based Controllers in building energy systems across a significant number of buildings. These control policies are simulated under three different scenarios that consider consumers with different pricing schemes and TES penetration.Efficient control strategies, have demonstrated significant potential, regardless of the presence of thermal storage and ToU pricing schemes, in reducing energy demand by 12.6%, cutting energy costs by 20.8%, and enhancing self-sufficiency and self-consumption, with minimal impact on Shared Energy. Implementing a flat tariff scheme under DRL enables consumers to increase their energy demand during periods of PV generation, which is particularly advantageous in a REC. Also, this approach lowers overall energy demand by 12.6% and boosts self-sufficiency, and it also decreases electricity exports from the REC to the grid by 18.2% compared to a ToU tariff scheme. When using ToU tariffs, thermal storage can be used to achieve cost savings, but total Shared Energy decreases, as do self-sufficiency and self-consumption of the REC. The results indicate that in a REC with high variable renewable energy and decentralized control, consumers using TES and ToU tariffs with peak prices during high irradiance periods may not be beneficial for the grid compliance.In conclusion, the coupling between DRL and thermal storage should be supported by more innovative pricing schemes for RECs and/or coordinated energy management, although it requires advanced communication and monitoring infrastructure.

Seasonal Behaviour of Arrivals and Prices of Major Crops in Krishi Upaj Mandis of Chhattisgarh, India

Chhattisgarh state is famous for its forest wealth and mineral resources and there is abundant production of crops. According to total area of crops, Paddy (77.47 percent) and Chickpea (5.43 percent) are major crops of Chhattisgarh state. Arrivals and price data (Year 2014-15 to 2023-24) of major crops were collected from website of Chhattisgarh state Mandi Board. The study was observed that the peak arrivals & price of paddy were started in the same months November to January in both market (KUM, Bemetra & KUM, Bhatapara) except peak price season starts in August to October with narrow difference in KUM, Bhatapara. In case of Chickpea, the peak arrivals and price started in the month of March to May & September to November, respectively. The Price stability of both crops (paddy & Chickpea) was higher in KUM, Bhatapara as compared to KUM, Bemetra. The less price fluctuation of paddy as compared to chickpea in Chhattisgarh state due to purchasing directly to farmers by the Government with Minimum Support Price (MSP) and also provided the Bonus. The study was suggested that the economic development of farmers in state, the government should purchase agricultural products other than paddy and cooperate in setting up dal mills & processing units. Necessary steps should be taken to increase of marketable surplus and development of community level threshing area. The study was importance in knowing the market behaviour and price fluctuation during the peak season and the major reason behind. The studies also throw light on the aspect how to enrich the economic condition of the farmers.

Dynamic feed scheduling for optimised anaerobic digestion: An optimisation approach for better decision-making to enhance revenue and environmental benefits

Anaerobic digestion (AD) offers a sustainable solution for clean energy production, with the potential for significant revenue enhancement through enhanced decision-making. However, the complexity and limited flexibility of AD systems pose challenges in developing reliable optimisation methods. Changing feeding strategies provides opportunities for efficient feedstock utilisation and optimal gas production, especially in volatile gas markets.To provide better decision-making tools in AD for energy production, we propose an integrated site model for the dynamic behaviour of the AD process in a biomethane-to-grid system and optimise production based on predicted gas prices. The model includes methods for optimal feed co-digestion strategies and integrates these results into a scheduling model to identify the optimal feedstock acquisition, feeding pattern, and potential gas storage operation considering feedstock availability, properties, sustainability, and fluctuating gas demand under different pricing variations.The methodology was tested on a 150 tonnes per day farm-scale AD plant in the UK, processing energy crops and manure considering both environmental (global warming potential) and economic objectives. The results showed strong adaptability of the proposed feeding schedule to the general trend of gas prices over time. To address the challenge of immediate price peaks, typically unattainable due to the system's sluggish behaviour and high retention times, the impacts of on-site storage were explored, leading to annual revenue increases ranging from 2 % to 7.4 %, depending on the pricing scheme, which translates to a significant boost in terms of revenue.

Economic management of microgrid using flexible non-linear load models based on price-based demand response strategies

Demand response programmes are used in microgrid research without considering the different price elasticity of distinct load types. To evaluate the impacts of demand response efforts, it is necessary to utilise a mix of nonlinear and linear models for creating load-responsive models in a manner that is realistic. With an aim to address this gap in research, an exhaustive technoeconomic investigation is being conducted to determine the effect that price-dependent demand response programmes have on the optimal scheduling of microgrids when linear and nonlinear load models are present. The flexible elasticity model is used to accurately describe how customers respond to changes in electricity price. Four load models, including exponential, hyperbolic, linear, and logarithmic, were constructed for each demand response programme. Rime is a newly created physics-based algorithm that has been deployed to handle the anticipated energy management problem that the microgrid may be experiencing. To evaluate the efficacy of the proposed strategy, four case studies based on grid price and participation scenarios have been carried out. The findings from our research are notably impactful, revealing an 8 % reduction in energy consumption when applying the critical peak price (CPP) load demand model. This efficiency gain translates into a notable enhancement in the load factor, increasing from 0.83 to 0.86, and a significant drop in overall generation costs from $25,463 to $21,823 under optimal conditions. These results vividly illustrate the practical value of our proposed research work, showcasing their ability to generate substantial energy savings and cost efficiencies in microgrid operations. The improvements in operational efficiency and cost-effectiveness underscore the potential of these models to refine microgrid management, delivering both economic and performance gains that are crucial for advancing sustainable energy solutions.

Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors

Efficient sizing and economic analysis of community battery systems is crucial for enhancing energy efficiency and sustainability in rooftop PV panel-rich communities. This paper proposes a comprehensive model that integrates key technical and economic factors to optimize the size and operation of the prosumer-owned battery, maximizing the financial returns over the life span of the battery. Sensitivity and uncertainty analyses were also conducted on a number of factors that are constantly changing over the years such as per-unit cost of the battery and interest rate. Monte Carlo simulations were utilized to replicate the unpredictable PV generations and the volatility of house load demands. The developed model is evaluated under three scenarios: a shared community battery for all houses, individual batteries for each house, and a combined system with an additional large load. Particle Swarm Optimization (PSO) is utilized to maximize the formulated objective function subject to the considered constraints. The findings indicate that integrating community batteries offered a substantial economic advantage compared to individual home batteries. The additional revenue stream of incorporating larger consumers looking to reduce their carbon footprint (e.g., commercial) returned a further augmented net present value (NPV). The influence of different tariff structures was also assessed and it was found that critical peak pricing (CPP) was the most prolific. The outcomes offer valuable insights for policymakers and stakeholders in the energy sector to facilitate a more sustainable future.

Do time-of-use prices deliver energy savings at the right time?

Time-of-use (TOU) electricity prices are increasingly being adopted to reduce consumption during the higher marginal cost afternoon hours. There is ample evidence that TOU rates reduce average consumption during the peak price hours of the day, but it is unknown how these energy savings are distributed across days. Using a unique dataset from households with smart thermostats, we find that adopting TOU rates causes large decreases in peak period AC usage, resulting in energy savings that are concentrated on the hottest, highest demand days when the benefits of conservation are the greatest.

Economic Scheduling Strategy for Multi-Energy-Integrated Highway Service Centers Considering Carbon Trading and Critical Peak Pricing Mechanism

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Impact of different CO2 price paths on the development of the European electricity system

In energy system transformation studies, the assumption of linearly rising CO2 prices is common and based on the decreasing issued European allowances (EUA). The actual auction price for allowances is influenced by market dynamics, resulting in non-linear variations in CO2 prices due to the possibility of banking allowances. This study explores the impact of non-linear CO2 price trajectories on the European electricity market. Various scenarios, such as increasing prices with the market interest rate, early price peaks, collapse, or fluctuations, are examined until 2030. The study quantifies the effects on power plant investments, profitability, consumer costs, and government revenues. The findings reveal that early and high price signals lead to an earlier transition with 7 % more wind power plants but at 4 % higher costs. Conversely, lower initial prices delay power plant investments in wind power plants by 6 % and result in 21 % higher emissions. Compensation for climate impact costs can yield a positive cost-benefit analysis in scenarios with early and high price signals when emissions are avoided through mechanisms like the Market Stability Reserve (MSR).

Decomposing momentum: The forgotten component

We split up the standard momentum return over months t−12 to t−2 at the highest stock price within this formation period. Of the overall momentum profits in month t, 84% can be attributed to the return prior to this peak price although research has exclusively focused on the post-peak return so far. The return predictability of the forgotten component is consistent with investor underreaction as underlying mechanism. Contrary to standard momentum strategies, the corresponding long-short returns are positively skewed, avoid momentum crashes, show no market state dependence, and yield consistent return premiums in both the US and international stock markets.

Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

Design and development of pilot plant applied to wind and light abandonment power conversion: Electromagnetic heating of solid particles and steam generator

With the rising capacity of renewable energy electricity but incomplete supporting dissipation equipment, this work develops a new charging and discharging device for electromagnetic heating of solid particles to convert electricity from renewable sources into superheated steam, which achieves battery storage efficiency with sufficient safety, terrain-independent and scalable through paralleling. The study reveals that the electro-thermal conversion efficiency of electromagnetically heated iron ore particles increases with particle mass flow rate at different wall temperatures, reaching a maximum of 93.8% at 500 °C. In contrast, the efficiency trend for quartz sand particles is inconsistent, peaking at 68% due to poor thermal conductivity causing inefficiencies at higher mass flow rates. A dimensionless mathematical model (Re-Nu, Re < 10) for both particles is derived from experimental data, offering theoretical guidance for industrialization. Experiments with a shell-and-tube solid particle-packed bed yield continuous production of 35 kg/h, 300 °C superheated steam for 12 min with an 86.4% discharge efficiency and achieve an 81% cycle efficiency, which surpasses compressed air storage(60%), approaches battery and flywheel efficiency(85%). Economic feasibility analysis for a 5MW/5MWH system under two business models demonstrates a short payback period of 2.62 years when providing industrial steam and 6.4 years when used in thermal power plant flexibility transformation. Technological innovations to improve equipment efficiency are critical to the first business mode (selling steam), while grid-determined peak prices are critical to the latter (thermal flexibility retrofits). The study underscores the technical and economic superiority of the proposed devices for utilizing abandoned wind and light, providing a novel solution for carbon reduction and peak shaving in the context of new energy.

Crypto-Mining and Electricity Market Nexus: Assessing the Evidence from the Nordic Electricity Prices

This paper examines the the dynamic relationship between the cryptocurrency market, through two major cryptocurrencies namely Bitcoin and Ethereum, and the Nordic electricity market. Specifically, it examines how the two main cryptocurrencies’ prices volatilities affect the Nordic electricity prices. Our analysis is based on daily spot prices from 08 August 2015 to 16 June 2020, using the dynamic conditional correlation DCC-GARCH model. Our results confirm the existence of a medium conditional correlation between cryptocurrencies and Nordic electricity market. Especially at the end of 2018, this correlation is significantly important. During this period, a peak in Ethereum prices was noticed which impact electricity prices via the high demand of power by miners who are attracted by such low prices, in the Nordic region, to increase the profitability of their activity.

Time-varying rates prompt different responses as a function of home energy efficiency

Spiking global energy prices make it all the more important to understand the distributional effects of commonly used energy transition policies such as financially-based demand management. We examine how building energy efficiency impacts the effect of time-of-use rates; peak prices could differently affect curtailment behaviour and bills in homes that require more continual energy use for comfort. Using a sample of 3,145 households in the Australian Capital Territory with panel data for up to five years, we use two-way fixed effects to estimate the effect of switching to a time-of-use (TOU) rate on household electricity use and bills, allowing for heterogeneous effects as a function of building Energy Efficiency Rating (EER). Analyses indicate that low-EER households dependent on electricity for heating curtail use when on TOU rates, while high-EER households do not. This does not appear to financially disadvantage low-EER households, but may impact comfort. Our findings suggest that the household responses to TOU rates can differ as a function of building energy efficiency for those households that are reliant on electricity for heating. This has implications for TOU rate design and demand management policies, particularly in the context of policies for electrification.

Price Behaviour and Price Forecasting of Tomato in Koyambedu Market, Tamil Nadu

This study aimed to comprehend the price behavior of tomato crops and forecast their prices in the Koyambedu market in Tamil Nadu, one of the major vegetable markets in South India. Secondary data on price of tomatoes were collected from Agricultural Produce Market Committees (APMCs) for a period of 8 years, from 2014 to 2022. Statistical tools such as trend analysis and seasonality indices were computed to understand the movement of prices in the Koyambedu market. Results revealed peak market prices in November and lowest in March, with a positive trendline indicating a gradual price increase over the study years. Comparative analysis between Autoregressive Integrated Moving Average (ARIMA) and Seasonal-ARIMA (SARIMA) assessed price prediction accuracy. The periodogram highlighted significant seasonality, guiding the use of SARIMA to forecast 2023 monthly prices. Predicted prices ranged from 3675.00 Rs/Qtl in November to 1178.76 Rs/Qtl in May.

Relationship Between Twitter Sentiment Analysis and Bitcoin Prices: Econometric Analysis of Long and Short Term Dynamics

The significance of social media in influencing cryptocurrency pricing has grown considerably in recent years. This study aims to explore the correlations between social media sentiments and Bitcoin pricing, both in the short and long terms, while also investigating the direction of these relationships. Sentiment analysis was conducted using the TextBlob model, which uncovers the underlying meaning in text through analysis. The study tested the hypothesis that there exists a relationship between sentiment analysis scores and Bitcoin prices over both short and long periods. Ensuring stationarity was crucial for time series analysis, involving the use of structural break and traditional unit root tests. Daily data from June 2021 to June 2022 was examined, with December 2021 serving as the focal point due to a peak in Bitcoin prices. The study focused on Bitcoin price data and sentiment analysis scores. Results revealed Twitter data as the dependent variable, showing no long-term relationship with Bitcoin prices. However, a significant and positive relationship was observed in the short term. This research contributes valuable insights into the intricate dynamics between social media sentiments and cryptocurrency pricing.

On grid-serving grid tariff design in Local Energy Markets

Local Energy Markets (LEMs) are virtual market places that allow for energy sharing of prosumers in proximity to each other. In LEMs, participants’ offers and bids are coordinated by a pricing mechanism, which sets price signals for generation and loads. This impacts distribution grid utilization. Dynamic grid fees could be used by the distribution system operator to add price signals for market participants that are beneficial to the distribution grid operation. To develop such a grid-serving LEM design, we propose a framework for the design of alternative grid tariffs as a trade-off between cost-reflective and cost-recovering grid tariffs. We analyse three alternative grid tariff components in the context of LEMs: time-varying energy fees, critical peak pricing (CPP) and capacity fees for power not traded on the LEM. A case study on a synthetically generated grid region shows that while the alternative tariff components can reduce the aggregated peak load of the region by up to 31% with the CPP, the fees can also lead to increased line utilization. Also, while total local added value can be increased with all alternative grid tariffs, especially capacity-based grid tariffs are connected to a strong redistribution of grid costs from flexible to inflexible prosumers.

The benefits of elastic fuel taxation

Current fuel taxation in France presents a blind spot, since it does not consider the oil price. This article explores the benefits of a tax rate 'elastic' to the price of a barrel of oil: if international prices soar, the tax rate would be lowered, and vice versa. Such a mechanism has generated limited literature, even though it could be of interest given the strong volatility of oil prices over the two last decades. We analyze the conditions of implementation and the implications of such an elastic tax, first from a theoretical point of view, and then empirically through a retrospective quantification of its effects, if it had been introduced in France from 2010. We conclude that an elastic tax could efficiently lower price peaks at the pump, thus increasing the acceptability of fuel taxes. The impact on public finances can be controlled and would be limited. With such a mechanism, the Yellow Vests movement might have been more limited, as well as the costly measures put in place in reaction to the war in Ukraine. This principle of elasticity in fuel taxation thus deserves the attention of all oil-importing countries, well beyond the French case.

Second-best electricity pricing in France: Effectiveness of existing rates in evolving power markets

The adoption of time-varying pricing in the French electricity market has been historically low despite their availability to consumers well before the deployment of smart metering. However, as the share of variable renewable electricity increases and carbon prices grow, the demand-side response will become increasingly important to achieve efficiency gains. Relying on the historical hourly consumption of French electricity consumers and multiple prospective weather years, we study the gain allowed by the broader adoption of time-varying electricity retail rates in low-carbon power systems. We develop a four-stage methodology to assess the efficiency and stability of the electricity supply cost component of the electricity bill in France for historical and prospective years. Our analysis demonstrates that peak pricing schemes have increasing interest in the context of further deployment of renewable capacity, capturing 25% to 50% of the welfare gain reached by real-time pricing schemes. The corresponding deadweight loss of peak pricing schemes represents 0.4 to 1 bn EUR per year in 2030 compared to real-time prices (RTP) schemes. Conversely, deadweight loss of current time-of-use (ToU) rates represents 1 to 1.2 bn EUR per year in 2025 and increases over time. The current design of TOU rates does not provide adequate incentives in future power systems. Besides the tariff efficiency, the results underlined an increasing price difference between on-peak and off-peak rates. This questions the social acceptance of time-varying retail rates for consumers unable to hedge against peak prices. By analysing the effectiveness and limitations of time-varying rates, our findings underline the importance and value of price-based DR in future power systems in general. Our results highlight the need to revise the end-user tariff design in the French power system, notably with regard to the conveyed price signals.