Intermittent Generation Research Articles

Market-based Real Time Congestion Management in a Smart Grid Considering Reconfiguration and Switching Cost

Network Real-Time Congestion (RTC) is a bottleneck that limits energy transfer from the generation units or up-grid to the loads. Some factors, such as intermittent generation of renewable resources and forced outages of generating units and load forecasting errors, can lead to Real-Time Congestion Management (RTCM) in a smart grid network. RTCM is a set of methods to eliminate congestion in real-time. To implement RTCM, some approaches can be employed, including network reconfiguration by Remote Control Switches (RCS), load shedding generation and up-grid power rescheduling. In this paper, a two-stage programming model is proposed to find the optimal solution for RTCM using the integration of reconfiguration and market-based approaches. Therefore, following the occurrence of congestion, at the first stage, microgrid central controller (MGCC) or central energy manager implements reconfiguration as the lowest-cost approach to mitigating RTC. The Soccer League (SL) algorithm is employed at the first stage to find the optimal network topology. Subsequently, based on the results obtained from the first stage, a programming model is applied at the second stage to completely eliminate the RTC. The proposed model minimizes a weighted objective function that includes the generation and up-grid rescheduling cost, load shedding cost, switching cost, and congestion clearing time. In order to model switching costs, a new index is defined to prevent risky switching and the depreciation caused by frequent switching. This index is determined based on the critical locations in the network and the age of RCSs. The numerical results demonstrate the efficacy of the proposed model.

A Distributed Algorithm for Controlling Continuous and Discrete Variables in a Radial Distribution Grid

The increased integration of distributed energy resources (DERs) in the distribution network with intermittent generation profiles will likely make voltage regulation a difficult task. However, DERs bring both challenges and opportunities, as they can provide renewable forms of local energy and act as voltage regulating components. The DERs are usually interfaced with power electronic devices, in which both their active and reactive power outputs can be regulated and treated as continuous control variables. In contrast, other voltage regulatory devices such as On-Load Tap-Changing (OLTC) transformers are often controlled by making discrete tap changes. Thus, appropriate control strategies are required to control and coordinate the DERs with other voltage regulatory devices. In this work, a distributed control strategy based on the Alternating Direction Method of Multipliers (ADMM) is developed, which controls both the continuous and the discrete variables in a distribution grid. The proposed control strategy is compared to a centralized and a local control architecture, where optimal parameters have been computed for the local controllers. Finally, a simulation study is made for the three different control architectures using a modified CIGRE medium voltage network. The results showed significant improvements in the daily voltage profiles while also reducing the power losses by over 30% when using an optimal control strategy.

A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin

With the growing percentage of the intermittent renewable power generation, the energy system is under increasing pressure in balancing the supply and the demand. As a major part of urban energy consumptions, buildings can provide considerable regulation flexibility for the energy system by actively managing their energy demands. For the building energy flexibility (BEF) provided by thermostatically controlled loads (TCL), its dispatch performance is vulnerable to the building thermal parameter errors, and in some cases, occupants need to provide the critical information related to the indoor temperature state and the occupancy state to the energy management system outside buildings, which decreases the availability of the BEF and raises privacy concerns. For these issues in the BEF utilization, this paper proposes a data-model fusion dispatch strategy based on the digital twin (DT). The proposed strategy is capable of parameter fault tolerance and privacy protection by combining the model-free advantage of the data-driven method with the analytical optimization advantage of the model-driven method. Firstly, a DT-based BEF dispatch framework is proposed. Secondly, the building DT is established by combining the building thermal dynamics (BTD) data-driven model and the TCL operation mechanism model. And the building response deduction is carried out based on the DT. Finally, under the rolling optimization framework, the data-model fusion dispatch strategy is devised by uniting the DT deduction and the optimization constructed by the BTD mechanism model, in which the multi-dimensional modeling of the BTD is carried out from the state dimension and the energy dimension. The simulation results show that the optimization result can reach 98.4% of the ideal result under the scenario with 15% parameter random error, and 98.3% of the ideal result under the scenario with 15% random state noise injection.

Energy distributing and thermodynamic characteristics of a coupling near-isothermal compressed air energy storage system

Intermittent renewable energy generation systems bring serious adverse impacts to the stable operation of the grid. In this regard, large-scale compressed air energy storage (CAES) systems with the potential to serve as long-term could be a solution for their optimal utilization. However, in a conventional CAES, most of the electricity is transformed into heat which passed into heat exchange mediums leads to serious heat transfer losses. In this study, a novel CAES system employing a Kalina cycle to effectively utilize the pre-compression heat in a near-isothermal compressed air process, is proposed and its holistic dynamic model is developed and presented for a deeper understanding of performance. The results showed that a near-isothermal compression undertakes the responsibilities of storing pressure potential energy, and the adiabatic pre-compression process helping in raising the inlet pressure of the liquid piston is primarily for storing thermal energy. In addition, the Kalina cycle working in the adiabatic pre-compression process achieves a 2.7 % improvement in the electrical efficiency. The thermal energy storage temperature was highlighted as the key factor in the energy allocation among the Kalina cycle, the thermal energy storage component, and the energy releasing sub-system in the proposed system. Through a systematic analysis of the proposed system performance, the highest electrical efficiency attained was 65.1 % with a 353 K storage temperature.

Probabilistic Load Flow of an Islanded Microgrid with WTGS and PV Uncertainties Containing Electric Vehicle Charging Loads

With the emphasis of utilities on decentralized power systems due to the considerable growth of renewable energy resources (RES), microgrids (MGs) as controllable small grids are need of an hour. For an objective analysis of microgrids with large grid parity of renewable energy sources, probabilistic methods are required along with the already established deterministic methods. A Gauss quadrature-based probabilistic load flow (GQPLF) method for microgrid has been proposed along with intermittent generation and uncertain loads, including electric vehicles (EVs). The distributed generation consists of wind turbine generation systems (WTGS) and photovoltaic systems (PVs) connected to 14 bus microgrid systems with six droop-controlled distributed generators. Extensive simulations have been carried out on a 14-bus microgrid with distributed generation and uncertain loads, and results are compared with the benchmark Monte Carlo simulation (MCS) method, which validates the efficacy of the proposed method. The time taken by the proposed GQPLF for the 14-bus microgrid system simulation is 60.9 seconds, which is significantly less compared to the benchmark Monte Carlo simulation method, in which time taken for a 14-bus microgrid system with 10,000 simulations is 9318.7 seconds. Comparing the proposed GQPLF method with the benchmark method validates its efficacy and efficiency.

A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation

Electricity markets are going through a comprehensive transformation that includes the large-scale appearance of intermittent renewable generators (RGs). To handle the local effects of new RGs on the distribution grid, the more efficient utilization of distributed local flexibility (LF) resources is necessary. However, the optimal market design is not yet known for LF products. This paper investigates a novel cost allocation mechanism in the context of this market challenge. The mechanism is designed to provide several important advantages of peer-to-peer trading without creating barriers to practical application. It provides partial disintermediation. The acquisition of LF remains the responsibility of the DSO, while the financial costs of the transaction are covered on power exchanges (PXs). To provide this functionality, the clearing algorithm of the PX in question has to incorporate a novel feature we call the Payment Redistribution Technique. This technique allows the buyers’ expenses to be larger than the sellers’ income, and the difference is used to finance flexibility costs. Its mathematical formulation is presented and analyzed in detail, considering computational efficiency and accuracy. Afterward, a realistic case study is constructed to demonstrate the operation of the algorithm and its energy market effects.

Microgrid Energy Management and Methods for Managing Forecast Uncertainties

The rising demand for electricity, economic benefits, and environmental pressures related to the use of fossil fuels are driving electricity generation mostly from renewable energy sources. One of the main challenges in renewable energy generation is uncertainty involved in forecasting because of the intermittent nature of renewable sources. The demand also varies according to the time of day, the season, the location, the climate, and the availability of resources. Microgrids offer a potential solution for the integration of small-scale renewable energy sources and loads along with energy storage systems and other non-renewable sources. However, intermittent generation and varying demand need to be matched to provide stable power to consumers. Therefore, it is crucial to design an energy management system to effectively manage the energy sources and supply loads for reliable and efficient operation. This paper reviews different techniques proposed in the literature to achieve the objectives of a microgrid energy management system. The benefits of existing energy management systems and their challenges are also discussed. The challenges associated with uncertainties and methods to overcome them are critically reviewed.

Experimental Assessment of a Novel Dual Opening Dewar for Use on a Liquid Air Energy Storage System Installed on Remote, Islanded, Renewable Microgrids

Islanded, renewable energy microgrids for use at remote operating facilities reduce logistical burdens associated with fossil fuel based electrical power sources and provide greater operational flexibility; however, energy generation can be intrinsically intermittent on renewable microgrids. This intermittent electrical generation can be mitigated with electrical energy storage. Liquid air energy storage (LAES) is one promising technology proposed to meet this energy storage issue due to its high energy density and lack of geographical constraints. Small-scale microgrids may not have enough excess capacity to store pressurized liquid air (LA), and instead may rely on unpressurized LA storage and their associated unpressurized power recovery systems. Using commercial off-the-shelf components, this article conducts a performance-based tradespace study for several dual opening, unpressurized Dewar designs for use with Stirling- or Peltier-based power recovery cycles. The dual opening Dewar design is found to be ineffective for the short-term LA storage times necessary for small-scale microgrid use, primarily due to excessive conductive thermal losses; however, the design may be useful as a LA receiver and immediate use energy storage medium for a connected Stirling generator. A proposed alternative solution using a self-pressurized Dewar for LA storage and transport for microgrid applications is presented for future work.

Stochastic optimization of trading strategies in sequential electricity markets

Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day-ahead and intraday trading decisions jointly for a portfolio with controllable and intermittent renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other.

Performance analysis of induction heated-porous thermochemical energy storage for heat applications in power systems

Power flexibility with fast and long-duration heat storage systems is crucial in modern power systems to meet the increasing cooling and heating demand and reduce the gap between renewable intermittent power generation and power demand. The hereby study analyzes the thermal and electrical performances of induction heated-porous thermochemical energy storage for heat applications into microgrids. The induction heating model is built with Maxwell equations for fast induction heat generation and the Surface-to-Surface (S2S) model, P1 approximation and Brinkmann model are adopted for induced and diffused heat transfer in the fluid phase and porous heat storage medium. The effects of the conductive plates’ orientation and location and the coil’s location are investigated. Furthermore, the effect of operating and structural parameters in terms of the electrical power supplied to the coil and the heat transfer coefficient of the coil’s cooling system and reactor insulation are sufficiently examined. Finally, essential parameters for the thermochemical material selection are assessed. It is found that the model with the highest magnitude of the induced magnetic flux passing through the conductive plates has outperformed at 107.57 % the worst model among the developed reactors. Moreover, the temperature distribution of the reactor resulted from the supplied power to the homogenized multi-turn coil and the heating rate at the heat storage material significantly increases with the supplied power. However, increasing the power supplied to the coil leads to higher conductive and convective heat loss resulting in less efficient electrical to heat conversion performance. Thermochemical materials with low emissivity should be preferred as the emissivity could lead to a 13.7 % improvement in the energy efficiency of the reactor. Implementing the proposed model in a microgrid for cooling and heating load improved the energy cost and self-consumption by 31.7 and 8 %, respectively.

A cooperative PSO algorithm for Volt-VAR optimization in smart distribution grids

The penetration level of photovoltaic (PV) systems is set to increase in the following years and already distribution networks (DNs) are straining to overcome the adverse voltage effects caused by reverse power flows and intermittent generation phenomena. Moreover, regular voltage optimization approaches that employ PV inverters as control devices, cannot deal effectively with the escalating dimensionality of the problem. This paper introduces a reactive power optimization method for PV-heavy DNs based on cooperative particle swarm optimization (CPSO). The proposed approach makes use of multiple swarms, each swarm containing a group of design variables that are interrelated with respect to the optimization objective; a community detection algorithm is employed to assign the design variables to the different swarms, by identifying voltage-decoupled zones of the grid. The different swarms cooperate by exchanging information in order to better explore the search space, while still solving the optimization problem as a whole. The feasibility and effectiveness of the proposed scheme are demonstrated through comparisons with other approaches for various load and generation scenarios on the IEEE 123-bus distribution system.

Energy management for solar-hydrogen microgrids with vehicle-to-grid and power-to-gas transactions

This study addresses the energy management of an integrated solar-hydrogen microgrid. The microgrid includes zero-emission vehicles, renewable energy sources, an electrolyzer, bidirectional charging stations, and a hydrogen refueling station with hydrogen storage. Vehicle-to-grid (V2G) charging stations can alleviate renewable electricity variability by discharging the energy of vehicle batteries back to the grid. The electrolyzer can absorb excess solar generation to balance supply and demand with power-to-gas (P2G) transactions. Given the uncertainties from intermittent renewable generation and energy demand, a two-stage stochastic optimization framework derives the optimal power management strategy for the component subsystems, aiming to minimize operating costs. The first stage of the model determines the day-ahead charging price and power supply for electric vehicle charging and hydrogen production, and the second stage performs real-time energy scheduling under different scenarios. The performance of the proposed optimization strategy is examined through a case study using real-world data, and the results demonstrate that daily operating costs can be reduced by up to 27.5%. Moreover, inclusion of the P2G process with hydrogen storage yields better performance to support peak shaving and reducing costs compared to using V2G or dynamic pricing-based demand response policies only.

LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management

Due to the unpredictable and stochastic nature of renewables, current power networks confront operational issues as renewable energy sources are more widely used. Frequency stability of modern power systems has been considerably harmed by fast and unpredictable power variations generated by intermittent power generation sources and flexible loads. The main objective of the power system frequency control is to ensure the generation demand balance at all times. In reality, obtaining precise estimates of the imbalance of power in both transmission and distribution systems is challenging, especially when renewable energy penetration is high. Electric vehicles have become a viable tool to reduce the occasional impact of renewable energy sources engaged in frequency regulation mainly because of vehicle-to-grid technologies and the quick output power management of EV batteries. The rapid response of EVs enhances the effectiveness of the LFC system significantly. This research work investigates a deep learning strategy based on a long short-term memory recurrent neural network to identify active power fluctuations in real-time. The new approach assesses power fluctuations from a real-time observed frequency signal precisely and quickly. The observed power fluctuations can be used as a control reference, allowing automatic generation control to maintain better system frequency and ensure optimum generation cost with the use of demand management techniques. To validate the suggested method and compare it with several classical methods, a realistic model of the Indian power system integrated with distributed generation technology is used. The simulation results clearly indicate the importance of power fluctuation identification as well as the benefits of the proposed strategy. The results clearly show a considerable improvement in response performance indices, as the maximum peak overshoot was decreased by 21.25% to 51.2%, and settling time was lowered by about 23.34% to 65.40% for the suggested control technique compared to other controllers.

Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control

Microgrids deal with challenges presented by intermittent distributed generation, electrical faults and mode transition. To address these issues, to understand their static and dynamic behavior, and to develop control systems, it is necessary to reproduce their stable operation and transient response through mathematical models. This paper presents a data-driven low-order model identification methodology applied to voltage characterization in a photovoltaic system of a real campus microgrid for secondary voltage regulation. First, a literature review is presented focusing on secondary voltage modeling strategies and control. Then, experimental data is used to estimate and validate a low-order MIMO (multiple input–multiple output) model of the microgrid, considering reactive power, solar irradiance, and power demand inputs and the voltage output of the grid node. The obtained model reproduced the real system response with an accuracy of 88.4%. This model is used for dynamical analysis of the microgrid and the development of a secondary voltage control system based on model predictive control (MPC). The MPC strategy uses polytopic invariant sets as terminal sets to guarantee stability. Simulations are carried out to evaluate the controller performance using experimental data from solar irradiance and power demand as the system disturbances. Successful regulation of the secondary voltage output is obtained with a fast response despite the wide range of disturbance values.

A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties

Microgrids have received great interest in achieving high renewable energy penetration. However, intermittent renewable generation may cause reliability problem (i.e., power inadequacy), especially for islanded microgrids. A comprehensive quantitative reliability assessment of microgrids considering uncertainties is essential. Current uncertainty-based reliability assessment approaches usually consider the uncertainties on the supply side only or additionally consider the uncertainties on the demand side using a general distribution without considering the uncertainty characteristics of different demands. This may lead to unreliable assessment results. Besides, existing reliability performance indexes cannot measure the probability/risk of power inadequacy under uncertainties. In this study, a novel uncertainty-based reliability assessment approach is therefore developed for microgrids considering uncertainties at both supply and demand sides. A new reliability index is proposed and a risk quantification method is developed to measure the risk/probability of power inadequacy under uncertainties. The uncertainties at both supply and demand sides are detailedly quantified using a bottom-up approach. The proposed reliability assessment and risk quantification method are tested on an islanded hotel microgrid in Hong Kong. The results show that the proposed reliability assessment approach can provide more robust reliability assessment results while the conventional approach has a probability of 8% to underestimate the maximum outage power. The proposed risk quantification method can help to provide more comprehensive reliability assessment results under uncertainties. The risk quantification results indicate that the highest monthly power inadequacy risk of the hotel microgrid appears in August (i.e., 5.8%), while the highest hourly risk appears at 9:00 p.m. (i.e., 4.8%).

A Hydrogen-Fueled Micro Gas Turbine Unit for Carbon-Free Heat and Power Generation

The energy transition with transformation into predominantly renewable sources requires technology development to secure power production at all times, despite the intermittent nature of the renewables. Micro gas turbines (MGTs) are small heat and power generation units with fast startup and load-following capability and are thereby suitable backup for the future’s decentralized power generation systems. Due to MGTs’ fuel flexibility, a range of fuels from high-heat to low-heat content could be utilized, with different greenhouse gas generation. Developing micro gas turbines that can operate with carbon-free fuels will guarantee carbon-free power production with zero CO2 emission and will contribute to the alleviation of the global warming problem. In this paper, the redevelopment of a standard 100-kW micro gas turbine to run with methane/hydrogen blended fuel is presented. Enabling micro gas turbines to run with hydrogen blended fuels has been pursued by researchers for decades. The first micro gas turbine running with pure hydrogen was developed in Stavanger, Norway, and launched in May 2022. This was achieved through a collaboration between the University of Stavanger (UiS) and the German Aerospace Centre (DLR). This paper provides an overview of the project and reports the experimental results from the engine operating with methane/hydrogen blended fuel, with various hydrogen content up to 100%. During the development process, the MGT’s original combustor was replaced with an innovative design to deal with the challenges of burning hydrogen. The fuel train was replaced with a mixing unit, new fuel valves, and an additional controller that enables the required energy input to maintain the maximum power output, independent of the fuel blend specification. This paper presents the test rig setup and the preliminary results of the test campaign, which verifies the capability of the MGT unit to support intermittent renewable generation with minimum greenhouse gas production. Results from the MGT operating with blended methane/hydrogen fuel are provided in the paper. The hydrogen content varied from 50% to 100% (volume-based) and power outputs between 35kW to 100kW were tested. The modifications of the engine, mainly the new combustor, fuel train, valve settings, and controller, resulted in a stable operation of the MGT with NOx emissions below the allowed limits. Running the engine with pure hydrogen at full load has resulted in less than 25 ppm of NOx emissions, with zero carbon-based greenhouse gas production.

Coordinated integration of distributed energy resources in unit commitment

Distributed energy resources, such as electric vehicles and roof-top solar panels have become increasingly popular and essential components of power distribution systems. However, their growth poses significant challenges to system operation, such as bidirectional power flow, intermittent power generation, increased peak demand, and unexpected frequency/voltage fluctuation. To tackle these challenges, we developed a vehicle-to-grid model, which incorporated dynamic electric vehicles usages, such as driving times, driving distance, and charging/discharging locations. The machine learning method of principal component analysis and XGBoost were used to develop the solar energy prediction model. We also developed a novel unit commitment model to coordinate and aggregate the distributed energy resources in power generation economic dispatch. Electric vehicle charging was used as elastic demand and electric vehicle discharging was used as power generation sources. The solar energy was considered as a negative load. Simulation results showed that uncontrolled electric vehicle charging and solar energy could negatively impact the power systems. For example, the load ramping rate was increased by 384%. Simulation results also showed that the proposed models could mitigate the negative impact and improve energy efficiency. For example, the peak demand, load ramping rate and average generation cost were reduced by 21%, 79%, and 27%.

QoS-Aware Data Center Operations Based on Chance- and Risk-Constrained Optimization

Because data centers are energy-intensive industries, data centers have made significant efforts to improve energy efficiency to save energy cost and utilize renewable energy to contribute to environmental sustainability. In addition, data centers have been considered as a potential application of demand response because they can possibly adjust energy consumption to process workload in response to time-of-use prices and intermittent renewable generation. Quality of service (QoS) should be guaranteed at the desired level to respond to users’ requests with their satisfaction in an effort to achieve energy-efficient and sustainable data center operations. Given this context, this study intends to develop a two-stage stochastic program that can be used to investigate optimal data center operations based on demand response that results in minimum energy cost with QoS guarantee against the stochastic workloads. Then, an additional chance constraint and risk constraint are considered as means of managing the level of QoS guarantee, and comprehensive numerical experiments are conducted with various parameter settings to evaluates the impact of the QoS guarantee with different levels on overall performance. The results show that the risk associated with the QoS guarantee for data centers needs to be properly managed to improve energy efficiency.

Adaptive model predictive scheduling of flexible interconnected low-voltage distribution networks considering charging preferences of electric vehicles

DC interconnection at the second side of distribution transformers helps achieve power sharing among nearby low-voltage distribution networks (LVDNs) and promote integration of intermittent inverter-based distributed generators (DGs). This paper proposes an adaptive model predictive scheduling method for flexible interconnected LVDNs considering charging preferences of electric vehicles (EVs). Firstly, the steady-state models of flexible resources including voltage source converters, energy storage systems along with AC and DC power flow models are established. Then, a model predictive control (MPC)-based rolling optimization model is formulated aiming to minimize the daily energy loss considering uncertainties of DGs, load and each charging station as a whole. To further explore the flexibility and dispatchability of each charging station, an adaptive MPC-based rolling optimization model is built considering three types of EVs with different charging preferences, i.e., uncontrollable EVs, charging-only EVs and vehicle-to-grid EVs. The scheduling window of the adaptive MPC-based scheduling is dynamically updated according to the maximum departure time of currently charging EVs to fulfill expected energy requirements of all EVs. Simulation results on a typical flexible LVDN show that the daily energy loss and total load fluctuation can be further reduced through real-time scheduling of controllable EVs in addition to existing flexible resources.

COVID-19, Generation Fuels, and the Energy Transition in the Philippines

The lockdown associated with COVID-19 caused a sizeable downward shock to electricity demand in the Philippines. Although coal is usually classified as a “baseload” fuel, it ironically bore the brunt of adjustment in the generation mix. The resulting upward pressure on retail prices was offset by force majeure contract provisions that allowed distribution utilities to pay lower fixed charges on their power purchase agreements. Coal generators thus suffered the double whammy of lower sales at lower rates. While existing coal plants will contribute to affordability during the recovery, plants in the planning stage may be reevaluated in light of the falling cost of wind and solar power and the low costs of dealing with intermittency when the percentage of intermittent generation is low. The Department of Energy’s “technology neutral” policy towards the generation mix is sound so long as least cost is interpreted to include the social costs of pollution. Some changes in renewable energy policy are indicated.