Renewable Portfolio Research Articles

Towards a sustainable power grid: Stochastic hierarchical planning for high renewable integration

Driven by ambitious renewable portfolio standards, large-scale inclusion of variable energy resources (such as wind and solar) are expected to introduce unprecedented levels of uncertainty into power systems operations. The current practice of operations planning with deterministic optimization models may be ill-suited for a future with abundant uncertainty. To overcome the potential reliability and economic challenges, we present a stochastic hierarchical planning (SHP) framework for power systems coordinated by a centralized planner. This framework captures operations at day-ahead, short-term and hour-ahead timescales, along with the interactions between the stochastic processes and decisions. In contrast to earlier studies where stochastic optimization of individual problems (e.g., unit commitment, economic dispatch) have been studied, this paper studies an integrated framework of planning under uncertainty, where stochastic optimization models are stitched together in a hierarchical setting, which parallels the deterministic hierarchical planning approach that is widely adopted in the power industry. Our experiments, based on the NREL-118 dataset, reveal that under high renewable integration, significant operational improvements can be expected by transitioning to the SHP paradigm. In particular, the computational results show that significant improvements can be achieved in several metrics, including system reliability, environmental sustainability, and system economics, solely by making a strategic choice to adopt the new SHP paradigm.

How does feed-in tariff and renewable portfolio standard evolve synergistically? An integrated approach of tripartite evolutionary game and system dynamics

In order to promote the healthy development of renewable energy, China has implemented Feed-in Tariff (FIT) and Renewable Portfolio Standard (RPS). However, due to the differences in natural conditions, social economy and renewable energy development, how does FIT and RPS evolve to achieve the best policy synergy is still a controversial issue. To explore the cooperation and evolution between FIT and RPS, we establish a tripartite evolutionary game model based on regulatory agencies, renewable energy companies and power grid companies to explore the evolution of FIT and RPS, and then combine a game model and a system dynamics model to simulate and analyze the policy effects of FIT and RPS. The findings of the study are: (1) Considering three aspects of the environment, economic benefits, and the consumption of renewable energy, RPS will replace FIT as the main RE policy if the introduction of tradable green certificate market is carried out in an orderly manner. (2) The faster the rate of decline of FIT subsidies and the rate of increase of RPS quotas, the faster the power market will reach stability. (3) It is essential to formulate a reasonable incentive and penalty mechanism in the regulation of the RPS system.

Measuring comprehensive carbon prices of national climate policies

ABSTRACT We measure the comprehensive carbon price from 2008 to 2019 resulting from climate policies imposed by 25 high-polluting countries that represent 82 percent of global carbon dioxide (CO2) emissions in 2019. Comprehensive carbon prices build upon previous notions—including explicit, effective, and implicit carbon prices—by incorporating a broad range of policies that reduce carbon emissions. We consider seven types of major market-based policies commonly used to create marginal incentives to reduce emissions: carbon taxes, emissions trading systems, fossil fuel taxes, fossil fuel subsidies, renewable portfolio standards, feed-in tariffs, and low-carbon fuel standards. Comprehensive carbon prices represent the weighted average of marginal incentives imposed on polluters by country policy mixes. We measure country-level comprehensive carbon prices by summing across all seven policies the product of (1) the marginal incentive imposed by a policy (expressed in dollars per ton of CO2 emitted), and (2) a CO2-emissions-coverage weight for that policy (equal to the proportion of CO2 emissions regulated by the policy relative to that country’s total CO2 emissions). We measure the global comprehensive carbon price by summing across all 25 countries the product of (1) each country’s comprehensive carbon price, and (2) that country’s proportion of CO2 emissions relative to total CO2 emissions from our sample of 25 countries. The global comprehensive carbon price has risen upward from +8.68 USD in 2008 to +19.13 USD in 2019, with a dip to +6.94 USD per ton in 2012. These values are far too low to appropriately address climate change. The comprehensive carbon price varies widely between countries, ranging from −128.35 USD to +146.25 USD in 2019. This heterogeneity means that polluters face uneven marginal incentives to reduce CO2 emissions. Our focus is on measuring marginal incentives rather than policy outcomes, such as overall emissions abatement, which we identify as a promising area for future research. KEY POLICY INSIGHTS Comprehensive carbon prices represent a useful metric for summarizing and tracking global and country-level efforts to mitigate carbon emissions through public policy. Comprehensive carbon prices report a dollar per ton of carbon dioxide emissions imposed by a wide range of climate policies including carbon taxes, emissions trading systems, fossil fuel subsidies and taxes, renewable portfolio standards, feed-in tariffs, and low-carbon fuel standards. Country-level comprehensive carbon prices vary widely, from −128.35 to +146.25 USD in 2019, reflecting wide heterogeneity in the overall effort countries are putting into climate mitigation. The global comprehensive carbon price has approximately doubled over the last decade, reaching a value of +19.13 USD in 2019, and remains too low to seriously address climate change.

Potential greenhouse gas risk led by renewable energy crowding out nuclear power

SummaryIncreasing variable renewable energy (VRE) is one of the main approaches for greenhouse gas (GHG) mitigation. However, we find a GHG increase risk associated with increasing VRE: VRE crowds out nuclear power (VRECON) but cannot fully obtain the left market share, which is obtained by fossil energy. We developed an integrated dispatch-and-investment model to estimate the VRECON GHG-boosting effect in the Pennsylvania-New Jersey-Maryland Interconnection and the Electric Reliability Council of Texas. In the above two markets, VRECON could increase the annual GHG emission by up to 136 MTCO2eq totally. Furthermore, we find that the VRECON GHG-boosting effect can be mitigated by combining wind and solar power. We argue that, for GHG abatement, policymakers should require the proper mix of wind and solar power in renewable portfolio standards and control nuclear power’s retirement pace to match the progress of VRE growth.

Reserve Cost Allocation Mechanism in Renewable Portfolio Standard-Constrained Spot Market

Governments have been taking policy instruments to enhance green power (GP) development, while the renewable portfolio standard (RPS) has become one of the most prevalent choices. Although RPS can guarantee GP producers’ income and expansion from a medium to long term perspective, curtailments are still inevitable, as RPS is integrated to power system by financial contracts and cannot affect market clearing. To solve this problem, the paper modifies the clearing procedure with a physical contract-based RPS restriction. However, along with that, reserve cost allocation mechanisms (RCAMs) ignoring RPS obligations are no longer cooperative, since GP penetration and market reserve demands are partially determined by RPS obligations now. Thus, an innovative RCAM, dominated by both net loads and RPS obligations through reserve costs contribution rate (RCCR) is proposed. A tri-level model is formulated to evaluate modifications and the proposed RCAM. On the first level, RCCR is achieved by calculating the incremental ratio of system reserve costs from level-1 without RPS constraint to level-3 with RPS constraint. The lower two levels form a classic bi-level model, where energy users interact with the independent system operator by strategical investment in distributed energy resources. Case studies based on the IEEE 6- and 118-bus system demonstrate the effectiveness of the proposed RCAM.

Economic Dispatch Considering Hourly Capacity Allocation with a Variable Renewable and Hydro-Based Generation Portfolio

The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.

Impact of Dynamic Thermal Rating on Optimal Siting and Sizing of Energy Storage Systems Under Renewable Portfolio Standards Requirements

Impact of Dynamic Thermal Rating on Optimal Siting and Sizing of Energy Storage Systems Under Renewable Portfolio Standards Requirements

The U.S. Manufacturing Sector's Response to Higher Electricity Prices: Evidence from State-Level Renewable Portfolio Standards

The U.S. Manufacturing Sector's Response to Higher Electricity Prices: Evidence from State-Level Renewable Portfolio Standards

The Impact of Renewable Portfolio Standards Targets on Power Investment, Production, and Transmission in China's Provinces

The Impact of Renewable Portfolio Standards Targets on Power Investment, Production, and Transmission in China's Provinces

Optimal Decision-Making of Trans-Provincial Electricity Market Subjects with Risks under Renewable Portfolio Standards

The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects. Therefore, it is beneficial to optimize the interests of each of these subjects, considering the unpredictable risks of renewable energy under the renewable portfolio standards (RPS) and researching their effects on the optimal decision-making of trans-provincial electricity market multi-subjects. First, we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricity market multi-subjects. Then, under the RPS, we construct a multi-subject game model of the power supply chain that recognizes the risks, and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market. Finally, we use MATLAB to verify the viability and efficacy of the proposed game model, and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects. In summary, we consider the uncertainty risks of renewable energy under RPS, study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decision-making of trans-provincial electricity market subjects, and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient, which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.

The Interactions between Renewable Portfolio Standards and Carbon Emission Trading in China: An Evolutionary Game Theory Perspective

The Interactions between Renewable Portfolio Standards and Carbon Emission Trading in China: An Evolutionary Game Theory Perspective

The Development of Renewable Energy Industry Under Renewable Portfolio Standards: From the Perspective of Provincial Resource Differences

The Development of Renewable Energy Industry Under Renewable Portfolio Standards: From the Perspective of Provincial Resource Differences

Moving China's Renewable Portfolio Standard Forward: Lessons from the U.S. Experiences

Moving China's Renewable Portfolio Standard Forward: Lessons from the U.S. Experiences

Effects of state and federal policy on renewable electricity generation capacity in the United States

Multiple studies have examined the impact of state-level renewable portfolio standards (RPS) on renewable electricity generating capacity. Two of the most widely cited of these papers (Yin and Powers, 2010; Shrimali et al., 2015) both conclude that as an RPS mandates higher shares of renewable generating capacity within a state, utilities respond by increasing the renewable electricity generating capacity. When we update the data for those models, we find that conclusion is not robust. We also modify those models and extend the datasets out to 2018, and in every model-permutation fail to corroborate the original conclusions. Finally, we examine the impact of the 2009 American Reinvestment and Recovery Act, and find it has had a significant, positive impact on renewable electricity generating capacity. We conclude that state-level RPS are not a sufficient substitute for federal action to reduce greenhouse gas emissions from the electricity sector. Federal leadership matters. This paper also demonstrates the importance of investigating the robustness of previous results to data refinements.

Renewable energy portfolio optimization with public participation under uncertainty: A hybrid multi-attribute multi-objective decision-making method

With the demand for energy increasing rapidly, the public is increasingly concerned about renewable energy (RE) planning. However, existing studies on RE planning rarely involved public opinions in the decision-making process. This study devotes to introducing a multi-attribute multi-objective decision-making model for RE portfolio selection with public participation. First, to reduce the complexity of the problem resulted from many participators, a linguistic risk appetite-based method is adopted to classify the public into subgroups. Considering that public opinions may be incomplete, the evidential reasoning approach is then used to aggregate the opinions of individuals within a subgroup. Next, the stochastic multi-attribute acceptability analysis method is applied to aggregate the preferences among subgroups and further derive a robust result regarding the public’s social acceptance of RE technologies. On the basis of the derived social acceptance, a risk-based fuzzy interval goal programming model is proposed to derive the optimal RE portfolio. Finally, an illustrative case of optimizing RE portfolios is given to demonstrate the applicability of the proposed model.

Risk-averse optimal bidding strategy considering bi-level approach for a renewable energy portfolio manager including EV parking lots for imbalance mitigation

In this study, a bi-level optimum bidding strategy is proposed in which a renewable energy portfolio manager (REPM), including electric vehicle parking lots (EVPLs) for imbalance mitigation, participates in the day-ahead (DA) market and balancing market (BM). In the upper-level problem, the total gain of REPM is maximized, while in the lower-level problem, the loss of each portfolio participant is minimized according to the specified reference value. The line capacities for bi-directional transactions with the grid are taken into consideration during the decision-making process of REPM. In the study, uncertainties for the DA market and BM price data, renewable energy productions, and electric vehicle (EV) behaviors are addressed by stochastic approach. The risk management is handled with the conditional value-at-risk (CVaR) method for loss or loss from profit that may occur due to uncertainties. Moreover, various case studies are carried out with Finland and Turkey electricity market data to prove the effectiveness of the proposed model.

Optimizing renewable energy portfolios with a human development approach by fuzzy interval goal programming

• Developing a systematic assessment framework for shaping a RE portfolio. • Considering human development as a primary parameter in RE strategic planning. • Extending the classic FIGP to provide the setting of interval coefficients. • Illustrating the proposed model's capability using a real-world scenario in China. Human development (HD) is considered one of the main metrics for determining the development level of a region. The central motivation of this research is establishing a strategic decision-making paradigm for optimizing renewable energy project (REP) portfolios, with a particular application to east China to quantify how HD influences a REP portfolio selection. The REP portfolio selection is subject to many unclear criteria and various sustainability goals. Fuzzy interval goal programming (FIGP) models are used to deal with the imprecision and uncertainty of the models' goals and coefficients. In this study, firstly, four objectives of social, economic, environmental, and technical criteria, as well as 12 sub-criteria were weighted by fuzzy ANP. Secondly, the potential REPs were assessed on these criteria to deal with the underlying ambiguities. Thirdly, the performance of potential projects was evaluated by the MABAC technique. Fourthly, a mixed-integer linear programming model was constructed and the FIGP is employed to form an optimal portfolio. Eventually, a sensitivity analysis was done to verify the validity of the suggested system. Based on this study's findings, HD is the most significant criterion. These innovative results will assist policymakers in better understanding the role of HD in the REP portfolio selection process.

Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience and hence profitability of future wind farms to a changing climate, given that current wind turbines have lifespans of up to 30 years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a clusterwise minimization of the Kullback–Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country.

Evaluation of Solar Energy using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method

Solar energy has grown tremendously in recent years. The result is cost reduction and Renewable energy development and Government policies favoring the use of both are due to technological improvements as a result. This study focuses on solar energy development and the technology of deployment, economic and policy aspects. This is more typical than the cost of energy technologies. Other renewable as with energy technologies, many countries offer tax concessions and exemptions, tariffs, and preferential interest rates, renewable portfolio standards and including voluntary tattoo energy schemes From financial and regulatory incentives Advantages of solar energy. Emerging carbon credit markets to harness solar energy is expected to provide additional benefits; However, Provided by current carbon market instruments of the Kyoto Protocol, etc. such as clean development mechanism the amount of incentives is low. Based on Method MOORA is some are commonly Used to solve material selection problems. The reference point approach Efficiency and Full Multiplicity MOORA Method are tested for perceived problems. To understand all three methods Very simple, are easy to implement and are give almost perfect rankings for material alternatives. The dimensionless ratios of the MOORA method avoid these objective normalization difficulties with all different units. In the first part of MOORA, These ratios are aggregated; second, they are a reference point used as distances. Results of the two parts control each other. It is a test of strength. In addition, MOORA multi-objective optimization shows strong dominance over all other methods. The Department of Facilities in Lithuania, Both parts of MOORA resulted in comparable rankings. The alternatives are Total Renewable power, Hydropower, Biopower, Geothermal, Solar PV, Concentrating solar thermal, and Wind power capacity. The evaluation Parameter is the years 2016, 2017, 2018, 2019, 2020, and 2021. The end result of solar energy is hydropower first grade and wind energy efficiency seventh grade.

Optimal strategies for a cost-effective and reliable 100% renewable electric grid

This paper explores cost-optimal pathways to 100% renewable power systems for the U.S. building stock. We show that long-duration misalignments of supply and demand, spanning from multi-day to seasonal timescales, present a dominant challenge that must be addressed to meet real-time 100% renewable targets. While long-duration misalignments can be addressed through energy storage, we show that alternative and readily available solutions that are more cost-effective should be considered first. Through a techno-economic analysis, we identify cost-optimal, region-dependent, supply-side, and demand-side strategies that reduce, and in some U.S. regions eliminate, the otherwise substantial capacities and associated costs of long-duration energy storage. Investigated supply-side strategies include optimal mixes of renewable portfolios and oversized generation capacities. Considered demand-side strategies include building load flexibility and building energy efficiency investments. Our results reveal that building energy efficiency measures can reduce long-duration storage requirements at minimum total investment costs. In addition, oversizing and diversifying renewable generation can play a critical role in reducing storage requirements, remaining cost effective even when accounting for curtailed generation. We identify regionally dependent storage cost targets and show that for emerging long-duration energy storage innovations to achieve broad adoption, their costs will need to compete with the decreasing cost of renewables. The findings of this research are particularly important given that most long-duration storage technologies are currently either uneconomical, geologically constrained, or still underdeveloped.