Generation Portfolio Research Articles

Quantifying energy flexibility of commuter plug-in electric vehicles within a residence–office coupling virtual microgrid. Part II: Case study setup for scenario and sensitivity analysis

Quantifying energy flexibility of commuter plug-in electric vehicles within a residence–office coupling virtual microgrid. Part II: Case study setup for scenario and sensitivity analysis

Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets

Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets

Comparison of power plant portfolios under the no energy mix target and national energy mix target using the mean–variance model

Comparison of power plant portfolios under the no energy mix target and national energy mix target using the mean–variance model

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

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

Measuring the effects of environmental policies on electricity markets risk

Measuring the effects of environmental policies on electricity markets risk

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

The optimized siting of grid-scale renewable generation is a viable technique to minimize the variable component of the electricity generation portfolio. This process, however, requires simulated meteorological datasets, and consequently, significant computational power to perform detailed studies. This is particularly true for countries with large geographic areas. Clustering is a viable data reduction technique that can be utilized to reduce the computational burden. This work proposes the use of Self-Organizing Maps to partition high-dimensional wind speed data using statistical features derived from Time-Of-Use tariff periods. This approach is undertaken with the view towards the optimization of wind farm siting for grid-support objectives where tariff incentivization is the main driver. The proposed approach is compared with clusters derived using Self-Organizing Maps with the temporal wind speed data for the input feature set. The results show increased cluster granularity, superior validation results and decreased execution time when compared with the temporal clustering approach.

Efficient use of the Generators for the Environmental Economic Dispatch from the energy system, including solar photovoltaic generation

The classic Economic Dispatch (ED) problem considers only the cost of power generation by thermal generators, often disregarding the safety parameters of the electrical network, environmental costs and especially the importance of predictive maintenance of the generators, when considering environmental costs in the optimization of ED this becomes a multi-objective problem Environmental Economic Dispatch (EED). Considering the global pressure to reduce emissions of pollutants in the atmosphere and environmental sustainability, incorporating the generation of Renewable Energies (RE) or Green Energy in the electricity grid is indispensable. Solar energy is becoming an important part of the power generation portfolio in many regions due to the fast decline in its costs and political incentives that favor the generation of clean energy sources. This article uses the Ant Lion Optimizer (ALO) method to solve the problem and EED restricted to the grid in a hybrid system (thermoelectric and photovoltaic). The results of the optimization problem were simulated in MATLAB. This research included 01 thermoelectric with 06 generators and 13 solar plants.

Information shocks and profitability risks for power plant investments – impacts of policy instruments

Information shocks and profitability risks for power plant investments – impacts of policy instruments

CVaR‐based generation expansion planning of cascaded hydro‐photovoltaic‐pumped storage system with uncertain solar power considering flexibility constraints

Abstract The development of a high solar energy penetrated power system requires considerable flexibility to hedge the risk of solar power curtailment and power shortage. This paper explores how the generation portfolio of cascaded hydro‐photovoltaic‐pumped storage (CH‐PV‐PS) generation system will be appropriately designed to balance the overall planning costs and operational flexibility constraints. The proposed study relies on the generation expansion planning (GEP) model of the CH‐PV‐PS system, considering a full set of flexibility constraints. An index designated ramp‐capability reserve shortage (RCRS) based on the conditional value at risk (CVaR) method is introduced to quantify the risk of solar power and load uncertainty. The piecewise linearization method and triangle method are developed to accommodate the non‐linear terms in the proposed model. Finally, the case studies are conducted to demonstrate the applicability and effectiveness of the proposed model.

Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation

Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.

The impact of development priorities on power system expansion planning in sub-Saharan Africa

Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO2eq emissions has a large impact on the generation portfolio and cost.

Applying risk tolerance and socio-technical dynamics for more realistic energy transition pathways

Many energy systems models have sought to develop pathways for deep decarbonization of the global energy system. Most often, these pathways minimize system costs or greenhouse gas emissions; with few exceptions, they ignore the constraints imposed by political, social, and economic factors that slow transition processes, making them prone to producing implausible decarbonization pathways. This paper integrates a key socio-technical factor—social acceptance of low-carbon nuclear power—into an energy systems model to illustrate how it alters the optimal energy generation mix. The United States was chosen as the example, but the approach itself is designed to be general and applicable to any region of interest. An empirically grounded risk tolerance model is developed to characterize acceptance of nuclear power and estimate an upper-bound deployment limit for the technology. Illustrative scenarios are presented to improve our understanding of how the socio-technical constraints that exist in the real world can alter deep decarbonization pathways. The cost-optimal generation portfolio to achieve net zero CO2 emissions by 2050 primarily relies on nuclear power. If risk tolerance concerns constrain nuclear deployment to socially acceptable levels, deep decarbonization scenarios are up to 11% more expensive than the reference scenario and require low-carbon options to be available and replace the reduced nuclear share. Results from this novel framework improve our representation of the effect of social acceptance on the adoption and diffusion of energy technologies. They also contribute to a growing literature that seeks to firmly embed the social sciences in climate and energy policy.

Understanding Small-Signal Stability of Low-Inertia Systems

Large-scale integration of renewable generation, usually interfaced to the network through power electronics, has led to drastic changes in power system dynamics. This paper presents novel insights into stability properties of such systems. For that purpose, a high-fidelity dynamic model of a generic low-inertia power system has been developed. The full-order, state-of-the-art control schemes of both synchronous and converter-based generators are included, with the latter differentiating between grid-forming and grid-following mode of operation. Furthermore, the dynamics of transmission lines and loads are captured in the model. Using modal analysis techniques such as participation factors and parameter sensitivity, the most vulnerable segments of the system are determined and the adverse effects of timescale coupling and control interference are investigated. More precisely, this work characterizes the maximum permissible penetration levels of inverter-based generation as well as the nature of the associated unstable modes and the underlying dynamics. Finally, potential directions for improving the system stability margin under different generation portfolios are proposed for several benchmark systems.

Preliminary analysis of long‐term storage requirement in enabling high renewable energy penetration: A case of East Asia

Abstract Integrating a high penetration of renewable energy for developing sustainable and low‐carbon electric energy system is becoming a common trend around the world. Many studies have evaluated the energy storage requirement for accommodating variable renewable energy (VRE). However, in the situation of high renewable penetration, there will be a huge potential requirement of long‐term energy storage for addressing the seasonal energy imbalance between VRE and load demand. Thus, a key element of evaluating the storage demand in enabling high VRE penetration is identifying the timescales of storage needed and the economic combination of long‐term and short‐term energy storages. This is neglected in existing researches. In this study, we quantitatively analyse the role of long‐term seasonal storage in enabling high VRE penetration. A generation expansion planning model is formulated to optimise the least‐cost generation portfolio with a renewable penetration target. Based on the proposed model, an empirical analysis for East Asia in 2050 is performed. The results indicate that (1) long‐term storage contributes to addressing the long‐term energy imbalance issue, (2) the optimal duration time of long‐term storage is around 720 h (a month), and (3) the long‐term storage becomes economical when the renewable penetration is above 70% (54.2% VRE penetration).

The Impact of Renewables on Operational Security: Operating Power Systems That Have Extremely High Penetrations of Nonsynchronous Renewable Sources

For decades, electricity transmission systems were planned to operate within the scope of active power transfers that, by their nature, were fairly well defined and limited in number, size, and direction. However, in recent years, this situation has been dramatically changing in front of our eyes as a result of two main developments. The first concerns new types of renewable power generation (i.e., wind, solar, tidal wave, and so on) and their increasing share of the generation portfolio. The second relates to the introduction and evolution of electricity markets. These factors contribute to fundamental changes in generation patterns and power transfers in ways that were not anticipated.

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is mainly due to the mitigation measures that include lockdown and work from home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-lockdown business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result in a net benefit for flexible stakeholders, such as consumers on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

Implications of Power Industry Marketization for Sustainable Generation Portfolios in China

Implications of Power Industry Marketization for Sustainable Generation Portfolios in China

A Supervised Learning Scheme for Evaluating Frequency Nadir and Fast Response Reserve in Ancillary Service Market

Power system operators evaluate the frequency security of the system by predicting the frequency nadir, which is assumed to indicate the impact of a sudden loss of a generating resource. Recently, frequency nadir prediction has become more challenging because renewables have penetrated and significantly changed the generation portfolio within the system. Conventionally, the frequency nadir is determined using a frequency response model where the features—load damping, system inertia, and effective governor response—are assumed to be known. However, these key features are not easily obtained in a power system that continuously changes during daily operation. This study proposes a supervised learning scheme that traces these key features. It also proposes a new feature—the power gap rate—that better reflects the influence of the load on the system frequency than that of the load damping. Feature importance recognition and the construction of a frequency nadir model (FNM) are realized using the proposed supervised learning scheme. The proposed FNM achieved 54% higher accuracy than the conventional method. Finally, the FNM is implemented in a planning process that quantifies the capacity of the fast responsive reserve (FRR). In two renewable penetration cases, the proposed FRR procurement successfully secured the frequency nadir above the security criterion.

A Probabilistic Methodology to Quantify the Impacts of Cold Weather on Electric Vehicle Demand: A Case Study in the U.K.

The wide adoption of electric vehicles (EVs) is required for deep decarbonisation of transportation sector. The lithium-ion batteries that power EVs are temperature sensitive and electric ranges reduce under cold weather conditions due to excessive need for heating the battery and the driver's cabin. Therefore, some drivers may need to charge more often during the same day. In this paper, probabilistic modelling and simulation of large-collections of EV charging are presented and the impacts on power generation portfolio is investigated for the case of the UK. Particularly, the extra energy and power demands are quantified across different regions. Extensive simulations reveal that cold weather necessities an additional 630 MW of peak power when compared to optimal ambient temperatures. Simulation results further show that the hourly carbon intensity of the power grids could increase by 25% during cold days with low renewable generation. The problems pertinent to temperature effects on EV charging require greater attention as EVs are becoming the main mode of transport in the next decade.

Multi-input, Multi-output Hybrid Energy Systems

Multi-input, Multi-output Hybrid Energy Systems