Optimal valuation of wind energy projects co-located with battery storage

To reach Carbon Peak in 2030 and Carbon Neutrality in 2060, China is developing renewable energy at a fast pace. Renewable energy enterprises will participate in the power market in an all-round way as China gradually improves its electricity market. Signing the Power Purchase Agreement (PPA) helps renewable energy companies to avoid market risk and achieve sustainable development. Therefore, a novel PPA pricing model is proposed in our research. Based on the theory of the Levelized Cost of Energy (LCOE), our model considers system operating costs in China’s dual-track electric power sector, which is both government-guided and market-oriented. First of all, key influencing factors of the PPA agreement are analyzed in view of the developments of the renewable energy and electricity markets in China. Next, the design of pricing strategies for renewable energy power plants to cope with market challenges is presented through a photovoltaic project case study. The results show that when the operating costs of the system are considered and other conditions remain unchanged, the investment payback period of the new energy power station will change from 10.8 years to 13.6 years. Furthermore, correlation degree and sensitivity coefficient (SAF) were introduced to conduct correlation analysis and sensitivity analysis of key elements that affect the pricing of the PPA. Finally, it is concluded that the utilization hours of power generation have the most significant effect on the PPA price, while the system’s operating cost is the least sensitive factor. The study expands the application of LCOE, and provides a decision-making solution for the PPA pricing of renewable energy power enterprises. It is expected to help promote power transactions by renewable energy companies.

Power purchase agreements aim to secure long-term contracts between sellers and buyers, particularly in renewable energy transactions. However, successful negotiations for a fixed long-term price and energy volume while ensuring maximum utility for stakeholders remains a significant challenge. This paper introduces a comprehensive model for negotiating 24/7 power purchase agreements, focusing on hourly pricing to address deficits and surpluses throughout each day of the contract timeline. The model incorporates demand flexibility through battery storage, settling on the strike price using Nash Bargaining theory and optimal management of energy consumption relative to market price fluctuations. A soft margin support vector machine classification model determines the buyer’s maximum acceptable price. Moreover, Gaussian process classification is employed to calculate a probabilistic, risk-adjusted strike price, enabling a data-driven approach to power purchase negotiations. The proposed model’s performance is demonstrated through a detailed case study of Norway, illustrating how demand flexibility can significantly lower long-term power purchase agreement contract prices. The analysis of yearly price trends indicates that incorporating flexibility resources in long-term energy contracts may lead to a reduction in strike prices by around 25%. Moreover, such flexibility enhances demand-generation matching, thereby increasing renewable energy transactions within such agreements.

The electrical systems needed for offshore wind farms to collect power from wind turbines-and transmit it to shore- will be a significant cost element of these systems. This paper describes the development of a simplified model of the cost and performance of such systems. The performance prediction accounts for losses as a function of the power produced in the wind farm and the length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore. The collection system-the medium-voltage electrical grid within the wind farm, and the transmission system-the high-voltage electrical connection to an on-shore transmission line-are treated independently in the model. Data sources for the model and limitations of the data are discussed, and comparison is made to costs reported by others. The choice of transmission system technology is also addressed. This electrical system model is intended for integration into a more comprehensive model of offshore wind farm design, cost, and performance that will be used for parametric studies and optimization of wind farm configurations. Because some concepts for future offshore wind installations in deep water use floating platforms, this paper briefly discusses the application of submarine cable technology to nonfixed termination points, a departure from current practice. Introduction The National Wind Technology Center (NWTC) of the National Renewable Energy Laboratory (NREL) in Golden, Colorado, has undertaken a series of concept studies to evaluate the cost and performance of offshore wind farms. The product of these studies will be a comprehensive model of offshore wind farm design, cost, and performance suitable for parametric studies and optimization of wind farmconfigurations. The overall goal of this effort is to helpidentify technology pathways for offshore wind energy development and deployment in the United States. Offshore wind farms present an attractive option because they allow for larger wind turbines that operate in higher wind resources than land-based [1,2]. Offshore installations are also more expensive, so an understanding of their performance, cost, and optimal configurations is needed. This paper is an overview of one of these concept studies. It focuses on the power losses in wind farm electrical power collection and transmission systems, as well as the costs of the system components and their installation. A hypothetical system was based loosely on the Horns Rev offshore wind farm in Denmark. Inquiries were made with manufacturers about electrical and cost data on the required components, which were then compiled in a spreadsheet model. The performance prediction accounts for losses as a function of power output of the wind farm and length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore.

Virtual storage plant offering strategy in the day-ahead electricity market

The goal of carbon peak and carbon neutralization has accelerated the pace of building a high percentage of renewable energy power systems. However, the uncertainty and volatility of renewable energy bring many challenges to the stable operation of electricity market. In the new context of power market reform, it is necessary to establish a better trading mechanism and price subsidy mechanism for renewable energy participation in the market. This paper analyzes the impact of renewable energy participation in electricity market on market operation, market equilibrium and bidding strategies of market players. Then, we propose the potential means to cope with the market risk of renewable energy, compare the market mechanisms and development policies of various countries, and summarize relevant studies on renewable energy participation in electricity market in countries around the world. Finally, the development of a high percentage of renewable energy participation in the electricity market of China is summarized and prospected.

This article expands on the seminal policy work by Anderson et al. (2007) and explores the impacts of a changing energy mix on derivatives and hedging practices 15 years on from their work in the Australian National Electricity Market (NEM). The NEM has undergone vast change since 2007 as variable renewable energy (VRE) has entered the market. This can have a significant impact on the contracting patterns and derivatives’ use of participants. A survey was undertaken to determine how market participants in the NEM conduct derivatives trading and hedging of their portfolios. Additionally, participants shared their views on the current state of energy policy and operation of the market. The results showed participants continue to use derivatives, as was previously documented, with increasing focus on power purchase agreements (PPA) for VRE contracting and a sharp drop in the use of traditional peak contracts. Nonetheless, there arose apprehension regarding participants' future capacity to hedge portfolios, given the diminishing liquidity within the derivative market due to the departure of dispatchable plants. This poses a difficult path for policy makers and regulators as a functioning and active derivative market is integral for the operation of an energy only market such as the NEM.

Battery storage units (BSUs) are usually used to perform a single function in most planning studies related to microgrids (MGs). This paper presents an effective methodology to use the BSUs to perform multi-function including supply/demand matching and energy arbitrage. This is done according to a system policy containing all possible scenarios to fully utilize the BSUs to maximize the benefit. In the proposed work, the optimal sizing of the MG system under study containing wind turbines (WTs), photovoltaic system (PV), BSUs, and diesel units (DUs) is obtained. The main objectives of the proposed methodology are; 1) minimizing the total costs of the MG, 2) minimizing the harmful gas emissions, and 3) minimizing the accumulated power difference between the generation from renewable energy systems (RESs) and the demand. Due to the stochastic behavior of the output from the RESs, the uncertainties of wind speed, solar irradiance, and temperature are considered in the study. Two modes of operation of the MG (grid-connected and islanded) and the demand side management (DSM) are also considered. The problem is formulated as a constrained nonlinear optimization problem and is solved using two metaheuristic optimization algorithms, Moth-Flame Optimization (MFO) and Hybrid Firefly and Particle Swarm Optimization (HFPSO). Moreover, the uncertainties in the different parameters are considered by using the Latin Hypercube Sampling (LHS) method to generate samples of wind speed, solar irradiance, and temperature. To examine the proposed methodology, different case studies are presented and discussed. Moreover, the results of the used two algorithms, MFO and HFPSO, are compared to show their effectiveness in solving the proposed problem and assure the optimal solution. The optimization problem is implemented and solved using MATLAB software.

This paper develops and tests the performance of a new structure for grid-connected permanent magnet generator (PMG)-based wind energy conversion systems (WECSs) that have battery storage units (BSUs). The new structure is called the split dc-bus, and it is designed to reduce the pulsations in PMG developed torque and fluctuations in the power delivered to the grid. The reduction in PMG torque pulsations is achieved by employing a 5-level ac-dc power electronic converter (PEC) as the generator-PEC, whose dc outputs are processed by a two-port active dc-link. This active dc-link charges the BSUs and supplies the discharging PEC. The reduction of fluctuations in the delivered power is achieved by discharging the BSUs at point-of-common-coupling (PCC). The generator-side PEC, dc-link, grid-side PEC, and discharging PEC, are operated by a supervisor droop controller. The split-dc bus PMG-based WECS is implemented for performance testing under different wind speeds and levels of power delivery. Test results demonstrate reductions in PMG torque pulsations, along with reductions in the power delivered to the grid. These features of the split-dc bus PMG-based WECS are found to be complimented with a minor sensitivity to the changes in wind speed and changes in the power delivered to the grid.

Economic feasibility of a wind-battery system in the electricity market with the fluctuation penalty

Fast penetration of the renewable energy sources and transition toward sustainable energy supply paradigm is currently an ongoing process. It comes with many environmental benefits, but it also creates many challenges. Distribution System Operators are forced to move on from the fit-and-forget approach and take more active role in operating distribution system to face problems such as bidirectional power flows, congestion and voltage limits violation. This article, taking into consideration that regulated entities cannot interfere with market activities, proposes interaction between distribution system operator and battery storage operator for flexibility procurement and investing in battery storage units at the most critical points in the network. Firstly, using AC OPF, most critical nodes are detected and then optimal energy and power capacity of the battery storage units are determined. Value of the flexibility service is determined by observing the profit difference from an optimal market participation strategy with and without network constraints.

Improving the utilization factor of a PEM electrolyzer powered by a 15 MW PV park by combining wind power and battery storage – Feasibility study

In April 2015, Singapore introduced an anonymous futures market for wholesale electricity. Using data on prices and other observable characteristics of all competitive retail contracts signed from October 2014 to March 2016, a larger average quantity of open futures contracts that clear during the term of the retail contract a month before the retail contract starts delivery predicts a lower price for the retail contract. This outcome is consistent with increased futures market purchases by independent retailers causing lower retail prices. Consistent with the logic in Wolak (2000) that a larger volume of fixed-price forward contract obligations leads to offer prices closer to the supplier’s marginal cost of production, a larger volume of futures contracts clearing against short-term wholesale prices predicts lower half-hourly wholesale prices. Both empirical results support introducing purely financial players to improve both retail and wholesale market performance. The paper then outlines how a regulator-mandated standardized futures market can be used as a long-term resource adequacy mechanism for the wholesale market regime.

Alberta’s electricity market is currently undergoing a period of substantial transition. The province should proceed with caution as it switches from an energy-only electricity market to a capacity market by 2021. Many other jurisdictions have already made the changeover and Alberta can learn from their experiences in order to avoid common mistakes and pitfalls that can arise with the deployment of a capacity market. There were growing concerns that the existing electricity market structure would not attract sufficient investment from conventional generation (e.g., natural gas) due to the increased penetration of zero marginal cost renewable generation. As a result, the Alberta government has chosen to transition to a capacity market. For consumers, a capacity market aims to ensure there is sufficient investment in new generation capacity to “keep the lights on” and reduce price swings in the wholesale market. The capacity market will also help the province meet its goals for attracting investors and transitioning away from its dependence on coal-fired electricity generation. However, a switchover is not as simple as it sounds. In an energy-only market, firms are paid solely based on the provision of electricity in hourly wholesale markets. In capacity markets, electricity-generating firms are also paid for providing generation capacity, reflecting the potential to provide electricity at some point in the future. While capacity markets can help ensure there is a reliable supply of electricity, there are several challenges in the implementation of capacity markets. This paper discusses the motivation for the adoption of capacity markets, highlights challenges regulators face when implementing this market design in the context of Alberta, and summarizes the key trade-offs associated with energy-only versus capacity market designs. Relative to an energy-only market, a capacity market is more complex and requires that regulators specify numerous parameters that are essential to the functioning of the market. An essential, but often controversial component is the formulation of the capacity demand curve. A capacity demand curve for Alberta has to be carefully designed to deal with uncertainties in demand growth, given that Alberta’s electricity demand is closely interconnected with the ups and downs of global crude oil prices. Consideration must be given to the perspective of outside investors who – as in any area of economic interest – are wary about uncertainty. Political and regulatory uncertainty can undermine the success of a capacity market. This potential for investor hesitancy could result in incumbent firms, familiar with investing in Alberta, seizing a larger share of the market in an already historically concentrated environment. It is critical that policymakers establish a clear and well-defined trajectory for the future of Alberta’s electricity market design as a whole, not just its capacity market. The capacity market is not a panacea for the potential downfalls of an energy-only market. There are trade-offs associated with both energy-only and capacity market designs. Energy-only markets are arguably more economically efficient with cleaner price signals. However, with political constraints on electricity price-spikes and the expansion of renewables, there is more uncertainty in an energy-only market’s ability to promote investment. A capacity market provides more certainty in terms of generation resource adequacy, but at a potentially higher cost. Despite these tradeoffs, capacity markets are unambiguously more complex. This places a heavy burden on regulators to carefully and correctly set critical capacity market parameters that can have substantive impacts on prices and the associated economic signals.

Preface. Acknowledgments. Figure Citations. About the Author. 1. Overview. 1.1 The Power Delivery Chain in a Vertically Integrated Utility. 1.2 The Power Delivery Chain in a Market Environment. 2. Energy, Load, and Generation Technologies. 2.1 Energy, Power, and their Measurements. 2.2 Load. 2.3 Generation Technologies. 3. The Grid. 3.1 Fundamentals: Load, Generation, and Alternating Current. 3.2 Grid Equipment. 3.3 Grid Reliability and Contingency Requirements. 3.4 Grid Confi guration. 3.5 Grid Operations. 3.6 Blackout August 14, 2003. 4. Short-Term Utility Planning. 4.1 Planning and Execution of Dispatch: Day-Ahead Planning Through Real-Time Delivery. 4.2 Day-Ahead Demand Forecasting: Load and Ancillary Service Requirements. 4.3 Least-Cost Dispatch in a Single Control Area: A Simple Model. 4.4 A Solution Using Profi t Maximization. 4.5 Least-Cost Dispatch in a Single Control Area with Operating Constraints. 4.6 Least-Cost Dispatch in a Single Node with Spinning Reserve and Regulation. 4.7 Least-Cost Dispatch in a Network. 4.8 Real Time. 5. Long-Term Utility Planning. 5.1 Project Development. 5.2 The Planning Process. 5.3 Long-Term Load Forecasting. 5.4 A Simplifi ed Look at Generation Capacity Additions. 5.5 Generation Additions and Retirements Within a Single Control Area. 5.6 Generation Additions and Retirements with Transmission to a Single Control Area. 5.7 Generation Additions and Retirements and Transmission Additions Within a Network. 5.8 Reserve Reuqirements. 6. Midterm Utility Planning. 6.1 Informational Requirements. 6.2 Formulation of the Optimization Problem. 7. A Market Environment. 7.1 Principles and Architecture. 7.2 Short-Term Market Design: Day-Ahead Scheduling Through Real-Time Delivery. 7.3 Long-Term Market Design: No Clear Solutions. 7.4 Midterm Market Design. 8. Asset Management in Short-Term Markets. 8.1 Retailers. 8.2 Power Producers. 8.3 Integrated Energy Companies. 9. Investment Analysis: Long-Term Planning in a Market Environment. 9.1 Investment Setting in Utility and Market Environments. 9.2 Project Analysis for a Merchant Plant. 9.3 Power Purchase Agreements (Long-Term Contracts). 10. Risk Management in the Midterm Markets. 10.1 Retailer Risk. 10.2 Power Producer Risk. 10.3 A Quick Risk Primer in Statistics for Risk Management. 10.4 Risk Management in Midterm Markets: Retailers. 10.5 Risk Management in Midterm Markets: Power Producers. 10.6 Risk Management in Midterm Markets: Integrated Electricity Suppliers. 11. The California Experience. 11.1 Market Fundamentals. 11.2 Short-Term Market Structure: The CALPX, CAISO, and Other Market Participants. 11.3 Fatboy, Get Shorty, Ricochet, and Death Star. 11.4 Market Contrast: PJM and California. Bibliography. Index.

IntroductionThe monitoring of the electricity market and preventing incorrect usage behavior are well-known issues in the competitive electricity market (see for example Marulanda et al., 2006; Nagypal et al., 2015; Strielkowski and Lisin, 2016; Nagaj, 2016) that are well-embedded into the strategy of sustainable energy development (Streimikiene et al., 2016). The monitoring of the electricity market plays a key role in the free electric energy market (Kasperowicz, 2011; Lisin et al., 2016). Supervisory actions are important for the System Operator and the Regulatory Commissions in order to ensure the efficient market performance. For Federal Energy Regulatory Commission in USA (Haymes, 2000), the market monitoring has been identified as a standard market design component and a basic function in the deregulated electricity market. According to Rahimi et al. (2003) and London Economics et al. (2007), the electricity market monitoring could be organized as an internal system or an institution in the Independent System Operators (ISO) structures, as well as could function also outside of the ISO structures and can provide service for ISO or other recipients (Goldman and Lesieutre, 2004).Furthermore, the availability of a well-developed monitoring system would also help agents to optimize their bidding strategies (David and Wen, 2000; Dun-nan et al., 2006; Pires Manso et al., 2015). Currently agents are concentrating on increasing their efficiency in order to maximize profits through a complicated system of product and sales management. Due to the fact that the process of decision making is very complex, whereas complete information is often unavailable, the electricity market monitoring appears to be difficult (London Economics, 2007). Therefore, if the market monitoring system functions properly, the transparency and reliability of the information concerning the electricity market dynamics may be ensured.The main activities of market monitoring as described in Rahimi et al. (2003), London Economics et al. (2007), (Dehdashti, 2005) and (Guler et al., 2005) include: 1) Detection of attempts to exercise market power and fraudulent behavioral, 2) Monitoring of market performance, 3) Identification of market design imperfection, 4) Transmission and generation blackout controlling, 5) Market participants' behavioral monitoring (activities and transactions).After an in-depth analysis of the existing literature devoted to exploring the issue of electricity market monitoring, it is possible to state that the information regarding the general methods of monitoring are available. However, there is a deficit of information related to the monitoring techniques focused on economical parameters of Spanish electricity market. Furthermore, the conducted analyses of the market structure and of the available source data related to Spanish power system have proven that that there is a need for a modification or a creation of new methods and indices, which shall be applied in order to correctly monitor the economical parameters of Spanish electricity market.This paper firstly introduces the concept and functions of the electricity market monitoring system (section I). Subsequently, the main methods and indices applied to the monitoring (market power, market performance and behavioral monitoring) are outlined (section II). Section III outlines the particularities of Spanish market. Subsequently, section IV presents conclusions.1. The global overview of the monitoring systemThe result of the extensive analysis of the existing literature on market monitoring is represented graphically in Figure 1.The Figure 1 presents the market monitoring framework: the elements and the correlation between them. This extensive analysis is based inter alia on the following sources: London Economics et al. (2007), Goldman and Lesieutre (2004), Dun-nan et al. (2006), Chakrabarti et al. (2008), Newbery (2004), Asgari et al. …