Generation Capacity Mix Research Articles

An optimization framework for capacity planning of island electricity systems

Meeting decarbonization goals requires taking significant action on electricity systems, which are a major source of CO2 emissions. Decarbonizing island electricity systems raises additional challenges due to their heavy historical dependence on fossil fuels and strict power-reserve and reliability requirements. To address this challenge, this paper presents a comprehensive optimization model for long-term capacity planning of island electricity systems. Our model determines an optimal mix of generation and transmission capacity to satisfy energy demand at least cost while respecting the strict technical constraints that are inherent in island systems. In addition, our model considers the use of thermal and renewable generation, electric vehicles providing electricity-system services, batteries, pumped-hydroelectric storage, and ac and high-voltage dc transmission lines.We demonstrate our model with a case study of the Canary Islands archipelago. Our results show that combining the aforementioned technologies reduces generation costs by up to 25% and capacity requirements up to 50% (relative to a case without the technologies). In addition, without any mechanism to internalize the social cost of carbon, fossil-fueled thermal generation is the lowest-cost source of energy. Environmental considerations demonstrate the benefits of renewable generation and result in these carbon-free energy sources supplying about 40% of the energy mix.

Distributionally Robust Optimization for Generation Expansion Planning Considering Virtual Inertia from Wind Farms

• The stability and reliability requirements in generation expansion planning are jointly considered. • A distributionally-robust-optimization-based generation expansion planning model considering the virtual inertia support from wind farms is proposed. • Frequency stability is guaranteed by inertia-based frequency requirements. • Balance between reliability and investment cost is achieved. High penetration of renewable energy generation imposes two significant challenges to power systems: the stability problem caused by low inertia and the reliability problem caused by generation uncertainties. Although these challenges have been widely recognized, their impacts on the optimal generation capacity mix have not been explicitly revealed and quantified. Luckily, with advanced converter control strategies, renewable generators may also provide the so-called virtual inertia similar to conventional inertia provided by thermal generators. This paper proposes a novel distributionally-robust-optimization-based generation expansion planning model considering the virtual inertia support from wind farms. The proposed model minimizes the generation expansion cost under uncertainty while maximizing the probability with which the system can fully absorb renewable energy generation. The constraints include expansion limits, unit commitment constraints, frequency requirements, virtual inertia provision, and distributionally robust joint chance constraints. The proposed formulation is recast into a mixed-integer second-order conic model and solved efficiently via commercial solvers. Case studies are carried on a 9-bus system and the IEEE 118-bus system to demonstrate the validity and scalability of the proposed method and highlight the importance of incorporating inertia response services

Assessment of electricity network investment for the integration of high RES shares: A Spanish-like case study

In the course of the energy transition, the EU member states’ National Energy and Climate Plans seek to install significant amounts of intermittent renewable generation capacity over this decade. Previous studies underline the social, political, and economic benefits of the electricity sector decarbonisation. The economic analysis of renewable energy sources (RES) integration is commonly performed with single-bus generation expansion models that seek the cost-optimal expansion of RES generation capacity to reduce operational expenses. However, electricity grids will require investments to adapt to the integration of high amounts of RES capacity. This paper contrasts the cost-optimal generation capacity mix obtained from a single-bus expansion model with a conservative estimation of electricity network investment requirements for an exemplary Spanish-like case study. RES network investment costs are put in context with an alternative non-RES generation expansion pathway. Network investment costs considered include expansion costs for both transmission and distribution grids. Electricity network expansion costs represent 6 to 10% of the corresponding generation capacity investment. Despite increasing network investment costs, the integration of high RES shares into electricity grids reduces operating costs when compared to non-RES pathways. Fuel and emission savings exceed total investments (generation capacity, network expansion, and connection costs). • We quantify the grid investments needed to integrate high amounts of RES. • Results show that RES drive significant grid expansion and connection costs. • However, incremental grid investments represent only 6%–10% of RES investment costs. • RES-driven reduction in operating costs compensate for incremental grid costs. • High RES penetration is found to reduce total system costs (investment + operation).

Optimal generation capacity mix in microgrid to meet demand

<p>This paper presents an approach for optimal generation capacity mix to fulfill future power demand using a micro-grid model which is operated in both the on-grid and off-grid modes. This is achieved using the solar photovoltaic (PV) system, fuel-cell, and battery energy storage system (BESS) with and without the grid-connected mode. Different control approaches and optimal size of the generators are presented. Proposed micro grid with solar PV system, solid oxide fuel cell (SOFC) and back scattered electron detector (BESD) is tested for different operational scenarios of loads. Comparative index of performance (CIP) is introduced to indicate effectiveness of the micro-grid operations in the off-grid mode. This is based on difference in the total harmonic distortions (THD) in both the on-grid and off-grid modes. This is established that CIP indicates that the micro-grid works efficiently in the both the on-grid and off- grid modes during the simulated events of the switching ON/OFF the loads at different test conditions. The optimal generation mix successfully met the load demand with and without grid having conventional generatio.</p>

Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system

Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island.

Optimal capacity planning of generation system integrating uncertain solar and wind energy with seasonal variability

This paper presents a generation capacity planning model for integration of utility-scale wind farms and grid-connected solar photovoltaic (PV) generation systems via multi-stage stochastic programming. A multi-stage scenario tree for available wind power, electric load, and solar irradiance is constructed with nine stages for a year. Random samples for wind, load, and solar irradiance are generated using Gaussian copula which represents correlation between random samples. Environmental energy policies to control carbon dioxide (CO2) emissions and increase energy generation from renewable sources are implemented, and the resulting generation capacity mix is investigated. A case study with a modified IEEE 300-bus system is performed. With the presented model, optimal generation capacity satisfying policy constraints in a future time was found within a reasonable amount of time.

A two-stage stochastic optimization planning framework to decarbonize deeply electric power systems

In 2015, 195 countries signed the Paris Agreement under the United Nations Framework Convention on Climate Change. To achieve the ambitious greenhouse gas-reduction targets therein, the electric power sector must be transformed fundamentally. To this end, we develop a two-stage stochastic optimization model. The proposed model determines the optimal mix of generation and transmission capacity to build to serve future demands at least cost, while respecting technical constraints and climate-related considerations. The model uses a mix of AC and high-voltage DC transmission lines, conventional and renewable generation, and different types of energy-storage units to meet these objectives. Short- and long-term uncertainties are modeled using operating conditions and scenarios, respectively.We demonstrate the model using a case study that is based on the Texas power system, with 2050 as the target year of the analysis. We include explicit carbon-emissions constraints. Doing so allows us to examine the effect of carbon-reduction targets and deep decarbonization of electricity production on investment decisions. As expected, we find that thermal-dominated power systems must transition toward having a renewable-dominated generation mix.

Integrating base-load cycling capacity margin in generation capacity planning of power systems with high share of renewables

The coordination between the renewable energy integration scale and the cycling capacity adequacy of power systems has been largely ignored at the capacity planning stage. In this paper, a generation capacity planning model (GCPM) for power systems with significant renewable energy is proposed taking into account the base-load cycling capacity margin (BCCM). In this model, the daily feasible ranges of system minimum output in the target year are given. For a day in the target year, if the sum of minimum load and BCCM does not reach the feasible range, then the BCCM constraint of that day is introduced into the GCPM to re-optimize the generation capacity mix. This treatment is conducted for each day to make sure the daily BCCM constraints can be satisfied in the capacity planning stage. Moreover, the impact of the CO2 emission cost on the optimal generation capacity mix is also addressed. The model is solved by combination of the screening curves method and Lagrange relaxation method. Numerical simulations are presented to verify the reasonableness and effectiveness of the proposed model. As compared with the traditional GCPM without BCCM constraints, the capacity shift from base-load plants to mid- and peak-load plants increases significantly with the increase of renewables after considering the BCCM constraints. It confirms that the BCCM constraints are non-negligible in GCPM, especially for power system with high share of renewable energy.

Assessing new transmission and energy storage in achieving increasing renewable generation targets in a regional grid

This study evaluates generation, transmission, and storage capacity needs to achieve deep renewable energy penetration in a regional electricity grid with an average load of approximately 20 GW. Increasing renewable energy targets are analyzed to evaluate the effects of realistic regional transmission upgrade and energy storage cost assumptions on the cost-optimal mix of generation, transmission, and storage capacity. Contextual data is used for New York State’s grid to examine how electricity generation from renewable energy resources (wind, water, and solar power) can meet between 50% and 80% of electricity demand. A central finding of the study is that when realistic transmission upgrade costs are assumed, new interzonal transmission and battery storage are not needed to cost effectively meet near-term renewable energy goals. In fact, New York can achieve 50% renewable energy penetration with only a buildout of new generation capacity: Onshore wind (13.7 GW), offshore wind (4.1 GW), and solar photovoltaics (3 GW). The presence of grid-scale battery storage, electric vehicles, or additional behind-the-meter solar capacity does not markedly change the model-selected generation mix. To achieve the 50% target, we compute a $52/MWh levelized cost of electricity for new renewable energy, which is in line with current generation costs.As the renewable generation target increases beyond 50%, the model begins to select transmission upgrades and new storage capacity, the latter particularly if battery costs continue to decline as anticipated. At deeper targets, marginal generation capacity would otherwise experience high curtailment primarily due to supply–demand imbalances; we calculate the value of energy storage at a 65% renewable energy penetration level to be 2.5–3 times higher than its value at a 50% level. However, the additional storage and generation – and transmission, to a lesser degree – needed to achieve longer-term renewable energy goals lead to a substantial rise in total investment. Between 50% and 55% targets, the computed marginal levelized cost of electricity for new variable renewable energy is $94/MWh, compared to $592/MWh between 75% and 80%, suggesting alternative integration measures are likely necessary at such high penetration rates.

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

A Study of Smart Grid Effects on Electric Vehicle Management Considering the Change of the Power Capacity Mix

Background: To understand the Electric Vehicle (EV) management effects deeply using Smart Grids (SGs) in the electric power sector, it is necessary to examine supply specifics such as the generation mix, generation costs, and CO2 emissions as well as the demand sector including peak load. This study attempts to comprehensively examine the changes in power supply and demand their effects in accordance with the degree of SG utilization, based on a scenario for the projection of EV roll-out in South Korea. Objectives: This study considers the change of the generation capacity mix as well as the change of power generation mix using the WASP model for the analysis of SG effects on EV management. In the scenario of the Korean government's EV deployment, this study has confirmed how electric power demand changes according to the degree of smart grid utilization. In addition, the WASP model has been used to examine not only the power generation mix but also the change in the installed capacity. Result: As a result, if the share of cost-effective and clean power generation sources is below the minimum load, the unit cost and CO2 emission could not be reduced together even though SGs are used to manage EVs. Conclusion: Increasing the share of power generation from clean energy sources to a level higher than that of the minimum load will allow EVs to become an eco-friendly means of transportation.

Review of planning methodologies used for determination of optimal generation capacity mix: the cases of high shares of PV and wind

It is an undeniable fact that energy systems all over the world are at the point of a paradigm shift as a need for decarbonisation is eminent and unavoidable. The pressure to decarbonise mounts year after year. Since two thirds of all anthropogenic greenhouse-gas emissions come from the energy sector, decarbonisation is more about reducing emissions in the energy system than any other system in the world. The increased need for decarbonisation has resulted in the increased installation of photovoltaic (PV) and wind systems in countries such as China, India, Germany, Ireland, Denmark, Japan and USA. The increased use of intermittent renewable energy resources introduces a need for advanced methods of planning because traditional planning methods give sub-optimal generation capacity mix when the electric grid is faced with high shares of variable renewable energy resources such as PV and wind. In light of this, this review highlights the major changes in planning methodologies when solving for optimal penetration of generation capacity in systems with high shares of PV and wind. The major highlights are placed on why the methodologies need to evolve as penetration levels of PV and wind increase and further highlight missing issues from the current advanced methods.

Quantification of Intra-hour Security-Constrained Flexibility Region

Rapid growth of renewable energy sources (RES) in the generation capacity mix poses substantial challenges on the operation of power systems in various time scales. Particularly in the intra-hour time scale, the interplay among variability and uncertainty of RES, unexpected transmission/generation outages, and short dispatch lead time cause difficulties in generation-load balancing. This paper proposes a method to quantify the intra-hour flexibility region. A robust security-constrained multiperiod optimal power flow model is first constructed to quantify the frequency, magnitude, and intensity of insufficient flexibility. The randomness of RES is captured by uncertainty sets in this model. The N-k contingency, spinning reserve, and corrective control limit constraints are included. This model is then cast into a two-stage robust optimization model and solved by the column-and-constraint generation method. The emergency measures with a least number of affected buses are derived and subsequently assessed by the postoptimization sensitivity analysis. Finally, the operational flexibility region is determined by continuous perturbation on the RES penetration level and the forecast error. The IEEE 14-bus system and a realistic Chinese 157-bus system are used to demonstrate the proposed method.

Level versus Variability Trade-offs in Wind and Solar Generation Investments: The Case of California

Hourly plant-level wind and solar generation output and real-time price data for one year from the California ISO control area is used to estimate the vector of means and the contemporaneous covariance matrix of hourly output and revenues across all wind and solar locations in the state. Annual hourly output and annual hourly revenues mean/standard deviation efficient frontiers for wind and solar resource locations are computed from this information. For both efficient frontiers, economically meaningful differences between portfolios on the efficient frontier and the actual wind and solar generation capacity mix are found. The relative difference is significantly larger for aggregate hourly output relative to aggregate hourly revenues, consistent with expected profit-maximizing unilateral entry decisions by renewable resource owners. Most of the hourly output and hourly revenue risk-reducing benefits from the optimal choice of locational generation capacities is captured by a small number of wind resource locations, with the addition of a small number of solar resource locations only slightly increasing the set of feasible portfolio mean and standard deviation combinations. Measures of non-diversifiable wind and solar energy output and revenue risk are computed using the actual market portfolio and the risk-adjusted expected hourly output or hourly revenue maximizing portfolios.

Multi-region optimal deployment of renewable energy considering different interregional transmission scenarios

Renewable energy is expected to play much more important role in future low-carbon energy system, however, renewable energy has problems with regard to load-following and regional imbalance. This study aims to plan the deployment of intermittent renewable energy in multiple regions considering the impacts of regional natural conditions and generation capacity mix as well as interregional transmission capacity using a multi-region dynamic optimization model. The model was developed to find optimized development paths toward future smart electricity systems with high level penetration of intermittent renewable energy considering regional differences and interregional transmission at national scale. As a case study, the model was applied to plan power generation in nine interconnected regions in Japan out to 2030. Four scenarios were proposed with different supporting policies for the interregional power transmission infrastructures and different nuclear power phase-out scenarios. The analysis results show that (i) the government's support for power transmission infrastructures is vital important to develop more intermittent renewable energy in appropriate regions and utilize renewable energy more efficiently; (ii) nuclear and renewable can complement rather than replace each other if enough interregional transmission capacity is provided.

Renewable energies impacting the optimal generation mix: The case of the Iberian Electricity Market

In the last few decades, electricity markets have undergone extensive reforms. A process of liberalizing electricity markets has been implemented by several countries, reducing the incumbents' market power. Thus, generation and retail are open to competition, while transmission and distribution remain regulated. Furthermore, the need to comply with the targets set by the Kyoto Protocol has boosted the installed capacity of renewable energy sources, such as wind power. Therefore, it is important to study what the impact of a renewable source like wind power will be on the optimal generation capacity mix, assuming producers can invest in other technologies and the wholesale market is open to competition. In this article, we assume two alternative generation technologies – wind and combined cycle gas turbines – as a simplifying assumption. We develop a two-stage model for the Iberian Electricity Market, where the choice of the capacity construction occurs in the first-stage, before electricity demand is known, and the optimal outputs and daily equilibrium prices are obtained in the second-stage, under the assumption that electricity demand does not exceed installed capacity. The application of this model reveals that producers can be expected to increase their renewable generation capacity (wind power).

Capacity Market Fundamentals

Electricity capacity markets work in tandem with electricity energy markets to ensure that investors build adequate capacity in line with consumer preferences for reliability. The need for a capacity market stems from several market failures. One particularly notorious problem of electricity markets is low demand flexibility. Most customers are unaware of the real time prices of electricity, have no reason to respond to them, or cannot respond quickly to them, leading to highly price-inelastic demand. This contributes to blackouts in times of scarcity and to the inability of the market to determine the market-clearing prices needed to attract an efficient level and mix of generation capacity. Moreover, the problems caused by this market failure can result in considerable price volatility and market power that would be insignificant if the demand-side of the market were fully functional. Capacity markets are a means to ensure resource adequacy while mitigating other problems caused by the demand side flaws. Our paper describes the basic economics behind the adequacy problem and addresses important challenges and misunderstandings in the process of actually designing capacity markets.

Climate-Aware Generation Planning: A Case Study of the Tamil Nadu Power System in India

The inherent unpredictability and high seasonality of wind and solar resources need to be factored into generation planning to develop a “climate-aware” generation plan that gets the balance right among different generation and import options. Both current and projected generation capacity mix in some parts of India do not reflect a good balance, with significant cost and reliability implications.

Optimal Generation Mix With Short-Term Demand Response and Wind Penetration

Demand response, defined as the ability of load to respond to short-term variations in electricity prices, plays an increasingly important role in balancing short-term supply and demand, especially during peak periods and in dealing with fluctuations in renewable energy supplies. However, demand response has not been included in standard models for defining the optimal generation technology mix. Three different methodologies are proposed to integrate short-term responsiveness into a generation technology mix optimization model considering operational constraints. Elasticities are included to adjust the demand profile in response to price changes, including cross-price elasticities that account for load shifts among hours. As energy efficiency programs also influence the load profile, interactions of efficiency investments and demand response are also modeled. Comparison of model results for a single year optimization with and without demand response shows peak reduction and valley filling effects, impacting the optimal amounts and mix of generation capacity. Increasing demand elasticity also increases the installed amount of wind capacity, suggesting that demand response yields environmental benefits by facilitating integration of renewable energy.

Intermittently renewable energy, optimal capacity mix and prices in a deregulated electricity market

This paper assesses the effect of intermittently renewable energy on generation capacity mix and market prices. We consider two generating technologies: (1) conventional fossil-fueled technology such as combined cycle gas turbine (CCGT), and (2) sunshine-dependent renewable technology such as photovoltaic cells (PV). In the first stage of the model (game), when only the probability distribution functions of future daily electricity demand and sunshine are known, producers maximize their expected profits by determining the CCGT and PV capacity to be constructed. In the second stage, once daily demand and sunshine conditions become known, each producer selects the daily production by each technology, taking the capacities of both technologies as given, and subject to the availability of the PV capacity, which can be used only if the sun is shining. Using real-world data for Israel, we confirm that the introduction of PV technology amplifies price volatility. A large reduction in PV capacity cost increases PV adoption but may also raise the average price. Thus, when considering the promotion of renewable energy to reduce CO 2 emissions, regulators should assess the behavior of the electricity market, particularly with respect to characteristics of renewable technologies and demand and supply uncertainties.