Optimal Generation Portfolios Research Articles

Power sector effects of green hydrogen production in Germany

The use of green hydrogen can support the decarbonization of sectors which are difficult to electrify, such as industry or heavy transport. Yet, the wider power sector effects of providing green hydrogen are not well understood so far. We use an open-source electricity sector model to investigate potential power sector interactions of three alternative supply chains for green hydrogen in Germany in the year 2030. We distinguish between model settings in which Germany is modeled as an electric island versus embedded in an interconnected system with its neighboring countries, as well as settings with and without technology-specific capacity bounds on wind energy. The findings suggest that large-scale hydrogen storage can provide valuable flexibility to the power system in settings with high renewable energy shares. These benefits are more pronounced in the absence of flexibility from geographical balancing. We further find that the effects of green hydrogen production on the optimal generation portfolio strongly depend on the model assumptions regarding capacity expansion potentials. We also identify a potential distributional effect of green hydrogen production at the expense of other electricity consumers, of which policy makers should be aware.

Planning low-carbon distributed power systems: Evaluating the role of energy storage

This paper introduces a mathematical formulation of energy storage systems into a generation capacity expansion framework to evaluate the role of energy storage in the decarbonization of distributed power systems. The modeling framework accounts for dynamic charging/discharging efficiencies and maximum cycling powers as well as cycle and calendar degradation of a Li-ion battery system. Results from a small-scale distributed power system indicate that incorporating the dynamic efficiencies and cycling powers of batteries in the generation planning problem does not significantly change the optimal generation portfolio, while adding substantial computational burden. In contrast, accounting for battery degradation leads to substantially different generation expansion outcomes, especially in deep decarbonization scenarios with larger energy storage capacities. Under the assumptions used in this study, it is found that battery energy storage is economically viable for 2020 only under strict carbon emission constraints. In contrast, given the projected technology advances and corresponding cost reductions, battery energy storage exhibits an attractive option to enable deep decarbonization in 2050.

Power system decarbonization: Impacts of energy storage duration and interannual renewables variability

Decarbonization of the electricity sector is one of the major measures in slowing down the pace of climate change. In this paper, we analyze the impacts of energy storage systems (ESS) and year-to-year variability and uncertainty in the hourly profiles of variable renewable energy (VRE) on power system decarbonization in 2050. We perform this analysis through capacity expansion optimization based on technology cost projections and CO2 emission restrictions based on 11 years of wind, solar, and load data variations in Italy’s power system, with a particular focus on how ESS changes the optimal generation portfolio and system performance. We also explore the impact of ESS duration on the renewables’ adoption and system costs. To quantify the impact of VRE variability in different years, we present a comparative analysis of capacity expansion optimization based on multiple-year and single-year data. Our results indicate a high RES penetration even in the absence of decarbonization policies, due to expected declines in future technology costs. In the transition to a zero-carbon electricity system, carbon capture and storage (CCS) with less than 100% carbon capture efficiency will play a role only in intermediate low-emission scenarios unless future technology advancements improve its capture efficiency. ESS investments contribute to lower total system costs by replacing more expensive flexibility resources. However, the value of ESS changes by its duration, with longer ESS durations having lower marginal value per added kWh storage capacity. Variability in VRE profiles leads to substantial variation in the system’s configuration and energy cost, depending on what year is used in the optimization. Decision making based on single-year data can therefore lead to substantial increases in the systems’ operational costs in other years due to increased probability of capacity shortages and load curtailments. In contrast, optimizing over multiple VRE and load years provides a more robust and cost-effective generation expansion strategy.

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four‐level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft‐linked investment and operational models, (3a) bidirectionally soft‐linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co‐optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics.This article is categorized under: Wind Power > Systems and Infrastructure Energy Systems Analysis > Economics and Policy Energy Policy and Planning > Economics and Policy

The effect of head-sensitive hydropower approximations on investments and operations in planning models for policy analysis

Planning for new generation infrastructure in hydrothermal power systems requires consideration of a series of nonlinearities that are often ignored in planning models for policy analysis. In this article, three different capacity-planning models are used, one nonlinear and two linear ones, with different degrees of complexity, to quantify the impact of simplifying the head dependency of hydropower generation on investments in both conventional and renewable generators and system operations. It was found that simplified investment models can bias the optimal generation portfolios by, for example, understating the need for coal and combined-cycle gas units and overstating investments in wind capacity with respect to a more accurate nonlinear formulation, which could affect policy recommendations. It was also found that the economic cost of employing a simplified model can be below 10% of total system cost for most of the scenarios and system configurations analyzed, but as high as nearly 70% of total system cost for specific applications. Although these results are not general, they suggest that for certain system configurations both linear models can provide reasonable approximations to more complex nonlinear formulations. Uncertain water inflows were also considered using stochastic variants of all three planning models. Interestingly, if due to time or computational limitations only one of these two features could be accounted for, these results indicate that explicit modeling of the nonlinear-head effect in a deterministic model could yield better results (up to 0.6% of economic regret) than a stochastic linear model (up to 9.6% of economic regret) that considers the uncertainty of water inflows.

Power system portfolio selection under uncertainty

We present a general methodology for power system portfolio selection under uncertainty in which fossil fuels and CO $$_2$$ market prices as assumed as the main sources of risk. The planning problem is developed by considering the power system as a whole in its interactions between dispatchable sources and intermittent renewables, under load demand and power capacity constraints. The portfolio selection is performed taking into account costs and benefits of the power system from a societal perspective. Efficient frontiers and optimal generation portfolios are derived and discussed. Based on USA data, an empirical analysis is developed to illustrate the main features of this approach.

Policy Making for Generation Expansion Planning by means of Portfolio Theory; Case Study of Iran

Given the pivotal role of electrical energy in any country’s socio-economic indicators and considering the complex structure power system, one of the prominent priorities of power industry policy makers appears to be expansion planning especially in generation section. Keeping in mind the intensely fluctuations of electrical energy generation costs, particularly those of fuel prices within the recent years and in the Middle East area, it looks impossible to opt for an optimal generation portfolio regardless of the risk of costs variations. Portfolio theory, as an efficient tool for risk management, provides a proper solution as to materializing the highest return, in view of the maximum acceptable risk, or contrariwise, the least amount of risk in view of the minimum expected return. This article aims to study the application of the theory in a challenge to formulate the optimal generation technologies portfolio, based on historical data on a span of 10 years in Iran. Meanwhile, it tries to study the effect of renewable energy resources on the optimal composition. The results of simulations indicate that the presence of renewable energy resources in the generation portfolio, ends in risk diminish for a specified expected return. This turns out to be a non-linear optimization problem; and GAMS and MATLAB software applications were put to use to solve it.

Optimization of India’s electricity generation portfolio using intelligent Pareto-search genetic algorithm

Optimization of power generation mix is a significant strategy of climate change mitigation for countries like India. This involves multi-objective optimization of cost reduction, emissions reduction and risk mitigation taking into account relevant constraints. We use a variant of portfolio optimization technique to generate India’s 12th five year plan electricity generation portfolio taking into account the carbon costs. For fitness evaluation of a generation portfolio, we use levelized generation costs and a Comprehensive Risk Barrier Index (CRBI), the latter capturing the cost risks modulated by project implementation barrier indices. For constrained optimization, we develop a fast hybrid algorithm, namely, Intelligent Pareto-search Genetic Algorithm (IPGA), which systematically evolves successively efficient frontiers and finally converges to the global Pareto-optimal front. This algorithm combines non-dominated sorting and separate elite population, while utilizing dual mode search for faster convergence and cluster reduction strategy for enhancing diversity. Halting mechanisms have been proposed for local and global Pareto convergence. We apply this generalized algorithm to simulate the impact of carbon costs, risks and barriers on India’s optimal generation portfolio.

Sub-Optimal Generation Portfolio Variance with Rate of Return Regulation

This paper demonstrates that continuation of traditional rate-of-return electric utility regulation of transmission and distribution assets will impede the ability of customers to optimize their generation portfolios. Under linear price regulation, with increasing (decreasing) returns to scale customers will choose a more (less) risky generation portfolio than they would with no transmission and distribution asset rate-of-return regulation. Similar problems arise under non-linear (two-part) pricing of transmission and distribution assets. When the per-unit price is set at marginal cost, with increasing (decreasing) marginal cost, customers will choose a more (less) risky generation portfolio than they would with no transmission and distribution asset regulation. With price caps the optimal generation portfolio is chosen.