Power Price Research Articles

A stochastic analysis of the energy and reserve operation for battery storage-assisted prosumer aggregator in the Southwest Power Pool Market

To facilitate the integration of renewable energy in electric power systems, the installed capacity of distributed renewable energy sources has been increased rapidly worldwide in recent years, and the prosumers and prosumer aggregators have become important players in smart grid. To maximize the economic benefits of a prosumer aggregator in power markets, a stochastic model used for generating the day-ahead (DA) energy and operating reserve scheduling strategies is proposed. The uncertainties associated with solar power productions, demands and prices in energy and operating reserve markets are handled via stochastic optimization technique. To manage the potential risks faced by the prosumer aggregator, the conditional value at risk is adopted to measure and manage its potential low profits or losses. Finally, case studies are performed by using the data in the energy and operating reserve markets operated by the Southwest Power Pool (SPP), and the optimal energy and operating reserve bids are generated effectively by solving the proposed model. The values of the operating reserve markets and battery storages are analyzed in detail via simulation, and it shows that higher reserve prices and larger battery storage capacity would lead to higher expected profit and lower risks for the prosumer aggregator.

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

Research Question: The launch and the beginning of trade on the South East Electric Power Exchange (SEEPEX) in Belgrade, early in 2016, opened the issue of forecasting volatility and price movements in the market. Motivation: The issue is of vital importance for all market actors for the purpose of maximising profits, reducing risks, planning production and making investment decisions. Forecasting volatility and price movements in electric power markets is important for traders with profit maximisation and yield-to-risk ratio optimisation in mind and, equally, for producers, large industrial consumers, investors and portfolio managers. Idea: Exploring models and techniques to forecast volatility in electricity markets and subsequently testing statistical methods based on time series data, the ARMA-GARCH being the preferred model, with a view to identifying optimal methods for this market. The volatility of the power market and price movements have been tested during a given period. The results can be used to gauge market parameters and opportunities to extrapolate future volatility and movements in electricity prices. Data: For the purposes of this analysis, a time series involving price movements and trade volumes were used, covering a period between the SEEPEX trade launch and the end of 2019. Tools: In the empirical part of the paper, "Stata 13" statistical and econometric software was used to explore stylised facts and model the volatility of SEEPEX electricity price returns. Findings: The authors offer an overview of different methods used in the research, having selected different specifications of the ARMA-GARCH model as the most reliable in predicting volatility in the given market. The exponential GARCH model with student-t error distribution is believed to have provided the best overall performance in modelling the SEEPEX return volatility, as well as the best volatility forecast. Contribution: This is one of the first empirical studies of the Serbian power market that deals with risk modelling. Forecasting time-varying electricity exchange volatility is important for all market participants interested in variance forecasts to be used to calculate risk and hedging measures.

Selection and Evaluation of Economic and Emission Reduction Benefit of a Mine Electrification System for Transportation in Deep Opencut Copper Mines

The Paris Agreement's global decarbonization drive has heightened the need for sustainable energy options, shifting focus away from fossil fuels. Copper, a crucial metal in this transition, will face increased demand, necessitating deeper opencut mines. However, this poses issues as traditional fuel-intensive trucks used for mine haulage result in substantial Scope 1 emissions and rising operational costs. This study addresses the urgency to explore alternative mine haulage systems that minimize fuel consumption and associated emissions while maintaining economic viability. It evaluates potential systems at "The Copper Pit," a Latin American copper mine, based on capital cost, operating cost, flexibility, safety, technological readiness, and emission abatement potential. Using the Analytical Hierarchy Method, weightage values for each criterion are calculated, resulting in a prioritized ranking table. Trolley Assist truck haulage emerges as the most promising alternative, showcasing impressive savings of up to 87% in fuel consumption and associated emissions, and an 80% reduction in running costs. Economic advantages are affected by diesel and power prices, with the carbon tax having the smallest impact. The study recommends a feasibility study for Trolley Assist implementation in the Copper Pit and a staged approach to minimize risks. Despite being less flexible and requiring upfront capital, Trolley Assist holds promise for deep copper mines, significantly reducing emissions and operating costs, ensuring sustainable and profitable mining operations.

Techno-Economic Analysis of Reversible and Paired Solid Oxide Cell Systems for Hydrogen Production

The United States (U.S.) Department of Energy (DOE) National Energy Technology Laboratory (NETL) has been pursuing the development of solid oxide cell (SOC) technology to enable future power generation systems with high efficiencies and low emissions. Government and industry net-zero goals are driving interest in approaches to decarbonize challenging sectors of the economy, such as industrial applications. Hydrogen (H2) – including that produced by SOC technology-based high-temperature electrolysis – is one promising route. At present, low prices for low-greenhouse gas intensity electrical power (i.e., renewable sources such as wind and solar) are available intermittently (at low-capacity factors), complicating the economics of clean hydrogen produced by electrolysis. Reversible solid-oxide cell (r-SOC) based power to gas systems have the potential to address this challenge by producing power when electricity prices are high and hydrogen when they are low. Plants with the ability to co-generate power from solid oxide fuel cells (SOFC) and hydrogen from solid oxide electrolysis cells (SOEC) are of particular interest for their dual revenue streams and the flexibility they can provide to the grid.This study investigates the cost and performance of two configurations of co-generating SOC plants. The first includes an r-SOC stack that alternates between power and hydrogen production. The second plant contains paired SOFC and SOEC stacks that are each dedicated to generating a single product when operated. For the purposes of this study, the plants are primarily intended to generate hydrogen, only switching to power production mode when market conditions are favorable. The study aims to elucidate expected tradeoffs between the higher capital cost of the paired SOFC/SOEC plant and the higher degradation rate of the r-SOC.The anticipated tradeoff of a reversible system is an increased degradation rate for lower capital costs. This is due to the paired systems being optimized for either fuel cell or electrolysis operation while the reversible system is not optimized for either. To quantify the impact of this, a base case where the SOFC, SOEC, and r-SOC all degrade performance-wise at the same rate (0.2% per 1000 h). The degradation rate of the r-SOC is then allowed to float to where the levelized cost of hydrogen (LCOH) of the two systems is at parity, which does not occur at the base case assumptions. Additional sensitivity analyses are performed on natural gas price and cost of imported power to determine which system is more favorable under different assumptions.In summary, the results show that the total plant cost of the paired SOFC/SOEC system is about 5% higher than the reversible system. The additional cost of having two separate SOC modules is largely diluted by the balance of plant costs. The LCOH of the r-SOC and paired SOFC/SOEC are ≈$3.60/kg and ≈$3.30/kg H2, respectively as shown in Figure 1. Despite the higher capital cost, the paired SOFC/SOEC has a more favorable LCOH. This is due to the SOFC’s ability to generate electricity cheaper than the assumed $60/MWh grid purchase price. Sensitivity analyses show how the assumed cost of grid power and natural gas price affects the disposition of hydrogen versus electron generation. As an example, the LCOH of the r-SOC becomes more favorable than the paired SOFC/SOEC at natural gas prices above $12/MMBtu and at electricity purchase prices below $27/MWh.In conclusion, this investigation shows that depending on the market situation and final application, reversible SOC units warrant consideration for inclusion in advanced integrated energy systems as grid electricity prices continue to decrease over time.For the purposes of this study, natural gas is used as the fuel for power generation operation, and the systems are equipped with high rate (≈98%) carbon capture. In future work, stored hydrogen may be examined as the fuel for power generation. Future analyses may also examine the use of industrial or other waste heat sources, as well as perform life cycle analyses for the cradle-to-gate greenhouse gas intensity for select system configurations and applications. Figure 1

A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network

Grid-edge distributed energy resources (DERs) present both operational challenges and opportunities to distribution system operators (DSO). This paper presents a day-ahead scheduling strategy for DER-rich active distribution networks to leverage operational flexibilities of various DERs for effective network management. The scheduling strategy is realized based on a DSO-centric day-ahead market model that incorporates a multi-period and two-stage optimal power flow (OPF) model to determine scheduling and pricing for network and non-network DERs. The uncertainties in day-ahead operation stemming from local generation, load consumption, and market prices are robustly considered for hedging the intermittency and forecasting uncertainties. A novel bid price function is proposed for local distributed solar PV generation aiming at maximizing participation in network service. A new reactive power management and pricing scheme is also presented for optimal scheduling of voltage support devices within distribution network. The proposed framework is implemented on IEEE-33 bus balanced test system and operational planning strategy is analyzed for overall cost-benefits of the system at various uncertainty levels. A 7-10 percent saving in cost of procurement for DSO can be accrued at various uncertainty levels, and a 10 percent increase in revenue for the PV generation can be observed.

Pricing inconsistency between the futures and Financial Transmission Right markets in North America

An empirical comparison of prices for two categories of financial derivatives of the NYISO, PJM and ISO-NE power markets, namely Financial Transmission Right (FTR) and futures contracts, is performed. The objective is to assess whether these two categories of derivatives are priced consistently, as their payoffs partially overlap. Statistical metrics reveal that implied prices for the loss component of the power price, which are obtained by combining information provided by both FTR and futures prices, are more volatile than corresponding realized loss values. This contradicts the idea of a price being a probability-weighted average of possible realized values, and therefore indicates the presence of the pricing misalignment between the FTR and futures markets of the three power markets during the period covered by the data sample.

Recovering fixed costs in the presence of prosumers

The existence of prosumers who concurrently produce and consume electricity with distributed renewable resources such as photovoltaic generation has been viewed as an effective way of fortifying grid resilience and enhancing sustainability. However, prosumers’ self-consumption is impacted by how surplus electricity is priced when feeding it into the grid. From the perspective of fixed cost recovery, this study compares market outcomes when their transactions with the grid are subject to net-metering and net-billing policies considering both the wholesale and retail markets. Under the net-billing policy, prosumers may face a power price lower than the retail rate or the wholesale price when selling their surplus energy into the grid. Compared to the benchmark case, where prosumers are subject to the same price signals as conventional consumers and producers, prosumers would over-consume under the net-billing case. The net-billing policy converges to the net-metering policy when prosumers purchase power from the grid. The analysis also finds that the net-metering policy can result in more wealth transfer from conventional producers and consumers to prosumers when compared to the net-billing policy.

Service-quality based pricing approach for charging electric vehicles in smart energy communities

Guiding electric vehicles (EVs) in smart energy communities to participate in demand response can significantly benefit users and the load aggregator. However, when the user's travel plans are uncertain, the EV may end up charging before the planned time, leaving the user with an insufficient power supply. This can occur because the control program chooses to charge the EV during periods of low electricity prices to reduce user costs. To address this issue, a new strategy for charging pricing based on service-quality is proposed to help users deal with the uncertainty of travel plans and promote user participation in demand response. In this paper, the proposed charging pricing of EVs consists of three parts. Firstly, the model of charging packages with different service qualities is designed for users. The differences in charging packages are reflected in the percentage of power supply and price within each time slot. Charging packages provide users with at least a certain amount of power in each time slot, allowing them to better cope with the uncertainty of their travel plans. Secondly, a subscription model for charging packages is built to match the user's travel plans. Finally, the pricing model of the charging package is established, representing the master-slave game between the load aggregator and the user. The model is linearized by the KKT condition. The results show that the proposed strategy helps users cope with the uncertainty of their travel plans, reducing charging cost for users by 16.39% while increasing revenue for the load aggregator by 14.8%.

The development of geothermal energy as a renewable power plant

Geothermal energy is a sustainable and ecologically beneficial energy source, it is believed that Indonesia alone has 40% of the world's geothermal energy reserves of roughly 28.000 MW. The Indonesian government expects the geothermal power plant installed capacity to reach 10.000 MW by 2025. However, the installed capacity remained at 1.739 MW until 2014. Aside from that, the Indonesian government has made significant investments to expand the geothermal sector through different current rules. This research aims to determine the absorption of geothermal energy as an alternative to power generation and many elements of the associated hurdles, such as natural and human resources. In addition, this paper also creates new model parameters that significantly improve model performance. Analysis of system dynamics methods and modelling and simulation methods are used for fast and accurate results. According to a literature analysis done by collecting secondary data from journals and associated research publications, existing conditions are judged insufficient to meet the installed capacity of geothermal energy with a target of 3.458 MW in 2025 based on simulation results of forecasts through 2050. Factors impeding progress include the government's lack of coordination and implementation difficulties. Furthermore, because the financial sector was redirected to cope with the economic crisis, the pandemic scenario in 2020 was one of the impediments. Based on these criteria, the optimum solution was sought by expanding installed electricity capacity and raising the selling price of geothermal power with a target of 24.5% and electricity output of 13.263 GWh.

Optimization of a multi-energy microgrid in the presence of energy storage and conversion devices by using an improved gray wolf algorithm

The growth of electricity consumption and demand for higher quality of electricity has directed the electricity industry towards using new technologies. In this paper, a novel model is proposed for the optimal scheduling of multi-energy microgrid (MEM) systems that rely on renewable energy sources. The model incorporates various emerging high-efficient technologies, including electric vehicle (EV) parking lots, power-to-gas (P2G) facilities, and demand response programs. The MEM system described in this paper comprises several components, such as wind energy generation, multi-carrier energy storage technologies, a boiler, a combined heat and power unit, power-to-gas (P2G) capability, electric vehicles (EVs), and demand response programs. The main objective of the system is to minimize the total operational cost. Moreover, the system operator has the opportunity to actively participate in the electricity, heat, and gas markets, enabling them to fulfill local energy demands and maximize profits through energy exchanges. The proposed system is exposed to uncertainties caused by wind power, demand, electric vehicles and power price. The optimization process in this study utilizes the Gray Wolf Algorithm, which has been enhanced by incorporating the Local Escaping Operator method. This improved algorithm is employed to optimize the scheduling and operation of the multi-energy microgrid (MEM) system. The goal of the Gray Wolf Algorithm, in combination with the Local Escaping Operator method, is to find optimal solutions that minimize the total operational costs of the MEM system while considering factors such as renewable energy generation, energy storage, demand response programs, and the integration of emerging technologies. The results obtained from the optimization process demonstrate a reduction in the total cost of operations when various technologies are integrated into the grid. By applying the proposed optimization model, which incorporates the Gray Wolf Algorithm improved via the Local Escaping Operator method, the overall operational costs of the MEM system are decreased. This reduction in costs is achieved by efficiently managing the integration of renewable energy sources, energy storage technologies, demand response programs, and other emerging technologies within the grid. Simulations are conducted for different scenarios, and the results show that integrated energy scheduling, along with the incorporation of emerging technologies and vehicle-to-grid capability, can reduce the total operating cost by 15%. Furthermore, the integration of multi-carrier energy storage systems with electricity-to-gas technology can reduce daily performance costs by up to 10%.

Dynamic scheduling of market price-based combined heat–power-constrained renewable microgrid

Abstract In this paper, a market price-based combined heat–power dynamic dispatch model for a microgrid is presented. The microgrid comprises cogeneration units and wind and solar power-generation units. A battery and a heat storage tank are incorporated to optimally balance variations in heat-and-power load demands. The proposed model explores the impact of market prices of electricity, heat supply and load variability on the optimal schedule such that profit maximizes and emission, loss and waste heat are minimized. The Weibull probability distribution function is applied to characterize the uncertain renewable power variable in the model and to find the over- and under-scheduling costs. The problem is solved using an improved differential evolution algorithm in which a fuzzy membership module is appended to obtain a solution having the highest attainment for the selected multiple objectives. The results show that the proposed model can handle uncertain heat–power demand and price scenarios to produce feasible and optimal schedules with owner profits, heat utilization and renewable share varying between 10.55–115.97%, 72.51–90.39% and 26.82–38.05%, respectively.

Electricity price modeling from the perspective of start-up costs: incorporating renewable resources in non-convex markets

This paper constructs a comprehensive electricity market model in the context of China, highlighting the deviation caused by neglecting start-up costs from an engineering perspective. The model allows for the abandonment of excess wind and solar power generation, contributing to the achievement of research objectives in scenarios with a high proportion of renewable energy. Our method innovatively integrates fuel and carbon prices, clean energy expansion, and power system marginal prices according to the carbon trading rules of the Chinese power industry, providing a more accurate representation of market dynamics. Findings reveal that neglecting start-up costs can lead to significant biases in electricity prices. We demonstrate that the marginal price sometimes deviates from the fluctuation of the real value. While fuel and CO2 prices can be transmitted downstream, the value of new energy must be transmitted through its impact on the marginal unit. This insight is crucial for understanding the “missing money” problem in electricity markets. Based on these findings, we propose policy recommendations. We suggest considering fixed and average costs as pricing benchmarks and utilizing capacity utilization as a signal for demand response to adjust power pricing. Furthermore, we recommend trading different energy types separately in the spot market with different pricing benchmarks to ensure the homogeneity of marginal units.

Planning distribution network using the multi-agent game and distribution system operators

When planning the distribution network, the income of each market entity is calculated by a fixed price. How to take the price of power into account while developing the planning strategy for each organization in the actual power market is an urgent issue that needs to be addressed imminently. To address this problem, considering the continuous change in the market price due to the change in the supply–demand relationship in the actual power market, this article proposes a distribution network planning method which considers the distribution system operator (DSO) and multi-agent game. First, the planning decision model of distribution network companies and power users with different interest subjects is constructed with grid planning and DG operation as decision variables. Second, DSO is introduced to the game framework. Based on the distribution locational marginal pricing (DLMP), a price accounting model is being developed. Then, the transfer relationships and game behavior among the distribution company, power users, and DSO are analyzed. Finally, an iterative search algorithm is used to solve a multi-agent game planning model of a distribution network that takes into account price signals in the power market. Examples based on IEEE 33-bus systems validate the suggested method’s validity and efficacy.

Trading behavior strategy of power plants and the grid under renewable portfolio standards in China: A tripartite evolutionary game analysis

China is facing the challenge of selecting appropriate policies to implement tradable green certificate (TGC) under the renewable portfolio standards (RPS). This paper proposes a tripartite evolutionary game model among thermal power plants, green power plants and the grid under the RPS and TGC policy to analyze the evolutionary stability strategies (ESSs) of unilateral subjects and systems and use numerical simulation to discuss the influence of different types of TGC purchase ratios and different trading markets on the choice of tripartite strategy. The results are as follows. (1) When the renewable energy consumption quota ratio is 21%, thermal power plants are more and more active in choosing to trade TGC as the ratio of wind power TGC and photovoltaic TGC gradually increases from 1:5 to 5:1. (2) In the TGC market where the price of wind power and photovoltaic TGC is 50 yuan, green power plants are more inclined to hold TGC; in the subsidized TGC market, where the price of wind power TGC is 190 yuan and the price of photovoltaic TGC is 650 yuan, green power plants tend to sell TGC. (3) With the price of excess green electricity consumption gradually decreased from 700 yuan/MWh to 100 yuan/MWh, the activity of three parties participating in TGC trading gradually increased, effectively activating the TGC market. Finally, from the perspective of policy management, we provide suggestions for the long-term sustainable development of the TGC market.

Techno‐economic, environmental and risk analysis of coordinated electricity distribution and district heating networks with flexible energy resources

AbstractDue to the strong dependence between electricity generation and heating supply and the need for optimal use of renewable energies to supply power and heat loads, it is essential to provide an integrated energy management structure for electricity and heating systems. This paper studies the techno‐economic and environmental aspects of a combined electricity distribution and district heating system by considering multi‐type flexible energy resources and the environmental and practical constraints of both networks. The integrated energy system (IES) is equipped with wind power plants, combined heat and power plants, gas boilers, heat pumps, multi‐energy storage systems, electric vehicle parking lots, and multi‐energy demand response, as well as the IES operator can take part in the energy market to supply thermal and electrical demands. Besides, a robust strategy is adopted to control the power price uncertainty in the day‐ahead scheduling of IES, where a robustness parameter is considered to adjust the risk level of scheduling. The numerical results show that technical and environmental constraints can significantly increase the operation cost of IES by 11.8% and 3.6%, respectively. In addition, multi‐type flexible resources can improve the operational flexibility of IES by 16.1%.

Quantitative energy trading strategies in cooperative microgrids in electricity market: A multi-dimensional analysis of risk and return

A quantitative energy trading model for cooperatively operating several connected microgrids (MGs) in the energy market environments is presented in the paper. The model emphasizes the coordination and management of coalition MGs and utilities to optimize smart grids to maximize their profit returns. Energy transfer plans are the sole control signals that are deployed for achieving optimum control within MGs. Case studies are used to describe uncertainty regarding renewable power production and load demands. Using conditional value-at-risk, the stochastic method reduces the likelihood of aggregators being affected by fluctuations in power prices associated with these uncertainties. Furthermore, the model evaluates the impact of cooperative operations on the performance of the MG portfolio during the lifespan of the scheme. The suggested risk-limited approach is validated by the digital twin and simulating a number of scenarios. According to the study findings, the suggested model limits the profit return variation possibility for MGs at the expense of a slight decrease in predicted profits.

Day-Ahead market model based coordinated multiple energy management in energy hubs

This paper presents a case-driven stochastic scheme for coordinated hubs energy management (EM) in a day-ahead market (DAM). In this scheme, electric, gas, and heat systems are connected through the consideration of gas and electricity for hub inputs, and electricity and heat for hub outputs. The paper considers combined heat and power (CHP) and energy storage systems, boilers, parking lots for renewable energy resources (RERs), and electric vehicles (EVs). In order to maximize the profit of hub in the DAM with consideration of linear systems and hub restrictions, a linear objective function has been included in the linear coordinated EM (CEM) problem. The scheme also considers the uncertainties in loads, DAM power prices, and RER output power. The grey wolf optimization algorithm is used to solve the proposed optimization problem. The efficacy and reliability of the proposed hubs' EM is examined in a simulation system based on the scheme. Lastly, outcomes indicate that the hub CEM is efficient across DAMs.

Supply-demand side management of a building energy system driven by solar and biomass in Stockholm: A smart integration with minimal cost and emission

As part of the transition to a sustainable future, energy-efficient buildings are needed to secure users' comfort and lower the built environment's energy footprint and associated emissions. This article presents a novel, realistic and affordable solution to minimize the footprint of smart building energy systems and enable higher renewable energy use in the building sector. For this, an intelligent system is being developed using a rule-based automation approach that considers thermal comfort, energy prices, meteorological data, and primary energy use. In order to lower the installation cost and part of the environmental footprint, batteries are not used, and the heat pump's size is decreased via component integration. Also, different renewable resources are effectively hybridized using photovoltaic thermal panels and an innovative biomass heater to increase the share of renewable energy, enhance reliability, and shave peak load. In order to secure feasibility, the suggested framework is assessed from the techno-economic and environmental standpoints for 100 residential apartments in Stockholm, Sweden. Our results show that 70.8 MWh of renewable electricity is transferred to the local grid, and the remaining 111.5 MWh is used to supply the building's needs and power the electrically-driven components. The biomass heater meets more than 65% of the space heating demand, mainly at low solar power and high electricity prices, illustrating the value of integration strategies to reduce the system's dependability on the local grid. The results further reveal that most energy purchases during the cloudy days and nights are repaid through the sale of excess renewable production during the warmer hours, with a bidirectional connection with the grid. The monthly energy cost is less than 140 $/MWh for most of the years. The cost can be held low due to the exclusion of batteries and minimizing the heat pump size. The proposed system has a low emission index of 11.9 kgCO2/MWh and can reduce carbon dioxide emissions by 70 TCO2/year compared to using the supply from the Swedish energy mix.

A Stackelberg Game-based robust optimization for user-side energy storage configuration and power pricing

With the rapid development of demand-side management, battery energy storage is considered to be an important way to promote the flexibility of the user-side system. In this paper, a Stackelberg game (SG) based robust optimization for user-side energy storage configuration and basic electricity price decisions is proposed. Firstly, this paper put forward a two-stage energy management framework considering the interactive relationship between the supplier-side system and the user-side system. Secondly, based on the two-part electricity price mechanism, a bi-level optimal sizing of user-side energy storage is established in which robust dispatching is considered to deal with the uncertainty of renewable energy. Thus, a three-layer optimization model of “pricing on the power supply side–basic scenario configuration on the user side–worst-case scenario scheduling on the user side” is formulated. Through relaxing the state variables of energy storage in the configuration and scheduling models and combining Karush-Kuhn-Tucher conditions, the user-side model is transformed into a single-layer problem. A distributed algorithm based on the method of bisection is used to solve the two-stage SG problem. The simulation results demonstrate the basic electricity price and energy storage configuration suggestions and prove the superiority of the proposed method.

A scheduling framework for VPP considering multiple uncertainties and flexible resources

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP.