Intermittent Power Generation Research Articles

Economic Analysis of Power & CO2-to-Methanol Systems for the Abatement of CO2 Emissions From an Industrial Plant

In this work, a Power & CO2-to-MeOH system is assessed, aimed at cutting CO2 emissions from a large-scale stationary source such as a cement plant. The assessed system comprises a cement plant with CO2 capture, a wind park for renewable power generation, a connection with the electric grid for electricity exchange, an electrolysis unit, the methanol plant and the final use of the methanol (which can be used either as a fuel or as a platform chemical). In addition, the system also includes hydrogen and CO2 storage units, to deal with the time mismatch between the intermittent renewable power generation and the continuous CO2 production from the cement plant with carbon capture. For each assessed scenario, the economic optimal size of wind park, electrolysis system and H2 storage with the constrain of converting the entire amount of captured CO2 is calculated. With the adopted assumptions that exclude CO2 capture from conventional methanol production, the results show that CCU allows achieving the highest CO2 emission reduction, while the CCS scenarios achieve the lowest costs. The breakeven carbon tax for the CCS scenario is about 49 €/tCO2. For the CCU scenarios to match the Reference Scenario without capture, the carbon tax should increase up to around 150-160 €/tCO2 in the chemical industry case and 320-330 €/tCO2 in the mobility case.

A Two-Layer Optimization Model for Energy Storage Configuration in the Distribution Network

The large-scale access of electric vehicles will aggravate the peak-to-valley load difference of the distribution network, and the intermittent power generation of high-penetration renewable energy also threatens the safe operation of the distribution network. Energy storage is widely used in the fields of power peak load shifting and access of renewable energy. This paper proposes a two-level optimization model that considers both planning and operation of energy storage system. At the planning level, a method for the location and capacity of energy storage is proposed based on the economics of the distribution network. At the operation level, a scheduling strategy applying to different seasonal scenarios is proposed to achieve the best effect of peak load shifting. The simulated annealing particle swarm algorithm with good convergence performance is used to solve the model. Finally, the performance and economic benefits of the model are demonstrated through simulation research.

Model predictive control for combined cycles integrated with [formula omitted] capture plants

Flexible thermal power plants integrated with CO2 capture systems can balance the intermittent power generation of renewable energy sources with low-carbon electricity. Among these power systems, natural gas combined cycles will play a fundamental role because of their faster operation and higher efficiency. Optimisation-based control strategies can enhance the flexible power dispatch of these systems and improve their performance during transient operation. This work proposes a model predictive control (MPC) strategy to stabilise these power plants with post-combustion CO2 capture based on temperature swing chemical absorption and provide offset-free reference tracking. A delta-input formulation with disturbance modelling is proposed, as it provides more efficient computation with offset-free control. Data-based models were developed to replicate the performance of the actual power and capture plants. Prediction of nonlinear behaviour was accomplished by creating a network of local linear models, which allowed the formulation of the dynamic optimisation program in the MPC strategy as a convex quadratic programming problem. A case study demonstrated the effectiveness of the proposed MPC to balance drastic changes on power demand and keep specified capture ratios. Furthermore, the reduced deviations achieved in the reboiler temperature suggest that the nominal value of this parameter could be increased to improve the desorption process without risks of reaching temperatures where the solvent would degradate.

Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration

Large penetration of wind energy systems into electric-grids results in many power quality problems. This paper presents a classification of power quality issues, namely harmonics and short-duration voltage variation observed due to the integration of wind power. Additionally, different techniques and technologies to mitigate the effect of such issues are discussed. The paper highlights the current trends and future scopes in the improvement of the interconnection of wind energy conversion systems (WECSs) into the grid. As the voltage variation is the most severe power quality issue, case studies have been presented to investigate this problem using steady-state time-series simulations. The standard IEEE test system namely IEEE 123-node test feeder and IEEE 30-node grid are solved under different operating conditions with wind power penetration. Typical daily load profiles of a substation in Riyadh, Saudi Arabia, and an intermittent wind power generation profile are used in all case studies. Mitigation of voltage variations due to wind intermittency is achieved using reactive power compensation of the interface inverter. The results show the effectiveness of these approaches to avoid voltage variation and excessive tap setting movements of regulators and keep the voltage within the desired operating conditions.

Modeling and Simulation of Solar Photovoltaic Renewable Energy Sources Power Generation System for MGs and Loss of Mains Detection

Power generation from Renewable Energy Sources (RESs) is steadily increasing due to a number of reasons. These include government subsidies on these RESs, improvement in technology in their manufacture and efforts to reduce greenhouse gas emissions. However, these RESs have associated challenges like intermittency in power generation hence unreliable power supply. Solar PV is gaining popularity as compared to other RESs due to its unique advantages in grid connected applications. It is therefore important to carry out accurate design and enough simulations on its dynamic behavior before actual installation at any given location using local environmental parameters. This paper discusses the detailed modelling, simulation and dynamic behavior of solar PV power generation systems for MG applications using Matlab/Simulink platform.

Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review

Solar photovoltaic (PV) power generation has strong intermittency and volatility due to its high dependence on solar radiation and other meteorological factors. Therefore, the negative impact of grid-connected PV on power systems has become one of the constraints in the development of large scale PV systems. Accurate forecasting of solar power generation and flexible planning and operational measures are of great significance to ensure safe, stable, and economical operation of a system with high penetration of solar generation at transmission and distribution levels. In this paper, studies on the following aspects are reviewed: (1) this paper comprehensively expounds the research on forecasting techniques of PV power generation output. (2) In view of the new challenge brought by the integration of high proportion solar generation to the frequency stability of power grid, this paper analyzes the mechanisms of influence between them and introduces the current technical route of PV power generation participating in system frequency regulation. (3) This section reviews the feasible measures that facilitate the inter-regional and wide-area consumption of intermittent solar power generation. At the end of this paper, combined with the actual demand of the development of power grid and PV power generation, the problems that need further attention in the future are prospected.

Optimal HVAC Control for Demand Response via Chance-Constrained Two-Stage Stochastic Program

This study is interested in the application of demand response (DR) to building energy management via heating, ventilation and air conditioning (HVAC) controls. The main objective of this study is to develop an optimization model to appropriately control HVAC operations to minimize cost in response to time-varying electricity prices and intermittent solar power generation. The main idea is to adjust electricity consumption associated with HVAC operations while ensuring the thermal comfort of occupants. In particular, we propose a two-stage stochastic program formulated to find an optimal HVAC operations schedule that comprises proactive and reactive controls to minimize energy cost against the stochastic nature in electricity prices, solar power generation, and weather conditions. Specifically, we introduce a chance constraint that can be appropriately integrated with the proposed optimization model to ensure thermal comfort at the desired level. To validate and evaluate the proposed approach, numerical experiments are designed to compare the proposed approach with the benchmark control policies designed to simulate the practice of the general purpose thermostat and the algorithm proposed by relevant study with various parameter settings. The numerical experiments show that the proposed approach results in significant cost savings while capturing optimal HVAC operations.

Grid-Tied Battery Integrated Wind Energy Generation System With an Ability to Operate Under Adverse Grid Conditions

An efficient control of a grid integrated microgrid with wind energy generating system (WEGS) and a battery storage, is implemented in this work. The quality of power injected to the utility is deteriorated with the level of unbalance and/or distortions present in the grid voltages. To compensate for the abnormalities, such as unbalance and distortions in the grid voltages, positive sequence components (PSCs) of grid voltages are extracted from their filtered $ \alpha \beta$ components. This work implements hybrid generalized integrator (HGI) filters for filtering the $ \alpha \beta$ components of grid voltages and also provides its 90 $\circ$ delayed signals, which are useful in generating PSCs. Phase and frequency of the grid currents are decided by the unit active templates derived from the PSCs of the grid voltages, therefore, high-quality grid currents are injected into the grid even for abnormal grid conditions. For faster dynamics during variations in load power, HGI filters are used for quick and accurate estimation of fundamental components of load currents. Intermittent power generation from WEGS is compensated for by integrating a battery bank into the system. Along with high-quality power injection, the grid side converter operates the microgrid in off-grid mode (during grid outages) and provides smooth grid integration once the grid reappears. A 3.7 kW speed sensorless synchronous reluctance generator is used in WEGS, which is controlled by the machine side converter. The control techniques are validated in MATLAB /Simulink platform, and experimental validation is done on a prototype developed in the laboratory.

Robust risk‐averse unit commitment with solar PV systems

A challenge for energy market operators (EMOs) is to render a cost-effective generation schedule, known as unit commitment (UC), for the power system with intermittent renewable power generations. Consolidating the risk management concept and robustness into the UC not only helps EMO to maintain the secure operation of power systems but also informs the EMO about the tail-risk cost. Based on this motivation, the authors have proposed a model for UC with worst-case conditional value-at-risk (UC-WCVaR) with intermittent solar generations and loads. Unlike intractable risk-averse stochastic UC models, this model minimises the worst-case CVaR of the dispatch cost over an ambiguity set of the probability distributions. This robust tractable model relies on the historical data that can partially characterise the underlying probability distributions of solar photovoltaic (PV) systems and loads. They solved this model by its equivalent tractable robust counterpart using linear decision rules. The tests conducted on the IEEE 118-bus test system demonstrate that: (i) UC-WCVaR solution matches the results derived from normally distributed solar samples; (ii) the UC-WCVaR is less conservative than the classic robust unit commitment. Also, the computation time is directly proportional to the number of uncertain resources and inversely proportional to the number of stages.

Strategic planning of renewable distributed generation in radial distribution system using advanced MOPSO method

The integration of wind turbine, solar photovoltaic based distributed generation in low voltage distribution network has many advantages. However, the output power from wind turbine and solar PVs are intermittent in nature. The production of these intermittent power generations may introduce many challenges in distribution network such as increase in power losses, voltage deviation and instability. Therefore, this paper proposes optimal integration of these intermittent renewable DGs along with biomass and capacitor banks to optimize distribution network parameters. The real time wind speed and solar irradiance data are processed in time series mathematical model along with relevant probability distribution function (PDF) for calculating the accurate power output from these renewable resources. Later, an advanced multi-objective particle swarm optimization technique has been used to optimize the distribution network parameters such as power losses, voltage deviation and voltage stability index. The proposed model has been validates and tested on Portuguese 94 bus system. Moreover, the proposed model results revealed the power loss reduction up to 77.82%, voltage deviation 9.68% and voltage stability index 44.25%.

A day-ahead joint energy management and battery sizing framework based on θ-modified krill herd algorithm for a renewable energy-integrated microgrid

The penetration level of intermittent power generation into power systems has been substantial during recent years, which in turn highlights the need for installing storage systems. Renewable energy sources have been widely integrated into distribution systems and microgrids. One effective solution would be utilizing battery energy storage systems, which can provide the system with various merits like ancillary services and enhanced power quality, mainly due to their high power density and fast response. Accordingly, the problem of resource scheduling of microgrids with volatile power generation and storage systems needs to be further studied, and an effective model should be presented. In this respect, this paper investigates the problem of day-ahead operation of a grid-connected MG, integrated with distributed generation units and storage systems. The problem has been modeled an optimization problem while the objective has been assigned to the model as the total cost minimization, subject to different constraints, both system constraints and assets’ constraints. Such constraints further complicate the original problem and an efficient solution method is required to tackle the problem as a large-scale optimization one. Thus, θ-modified krill herd approach is employed to solve the problem and provide the decision maker with an efficient solution. The simulation has also been conducted using a test MG and the results, obtained have been validated by comparing the results obtained from the presented method and those ones, derived from some well-known optimization algorithms.

Precursor salts influence in Ruthenium catalysts for CO2 hydrogenation to methane

The intermittency in power generation that characterizes renewable energy sources requires a way to convert the energy surplus. Among all the possibilities, the conversion of power in hydrogen via water electrolysis and then into methane via CO2 methanation represents a competitive storage system. CO2 methanation is an exothermic reaction which requires the use of low temperatures in order to achieve sufficiently high conversions: for this reason, there is a strong need in low-temperature active catalyst. In this work, several Ru/CeO2-ZrO2 and Ru-Ni/CeO2-ZrO2 were prepared and compared with Ni/CeO2-ZrO2, in order to evaluate the effect of Ru loading and Ru precursor salt. The results showed that in monometallic formulations the higher was the Ru amount the better were the reaction performances achieved, particularly at low temperatures. In bimetallic formulations, the presence of Ru enhances the catalyst activity; in particular, the use of the Ru acetylacetonate, for the deposition of the noble metal on support, remarkably reduces the catalyst onset temperature. The effect is due to the templating effect of the precursor molecule, which allows a better dispersion of the active compounds.

Business Models and Profitability of Energy Storage.

SummaryRapid growth of intermittent renewable power generation makes the identification of investment opportunities in energy storage and the establishment of their profitability indispensable. Here we first present a conceptual framework to characterize business models of energy storage and systematically differentiate investment opportunities. We then use the framework to examine which storage technologies can perform the identified business models and review the recent literature regarding the profitability of individual combinations of business models and technologies. Our analysis shows that a set of commercially available technologies can serve all identified business models. We also find that certain combinations appear to have approached a tipping point toward profitability. Yet, this conclusion only holds for combinations examined most recently or stacking several business models. Many technologically feasible combinations have been neglected, indicating a need for further research to provide a detailed and conclusive understanding about the profitability of energy storage.

A Comprehensive Review on Power Converters Control and Control Strategies of AC/DC Microgrid

Owing to the smart lifestyle, environmental consciousness, and dwindling fossil fuel supplies, there is a huge demand for clean and green energy. Microgrid (MG) is a crucial approach to renewable and clean energy. Because of the success of the AC utility grid and the growing demand for critical loads, it is very convenient to provide AC/DC MG that can easily satisfy both the alternating current (AC) MG and the direct current (DC) MG requirements. Due to uncertainty in load variations, main grid failures, intermittent power generations from renewable energy sources (RESs), the synchronization and interconnection of different power converters are the paramount issues in the control of AC/DC MG. This article presents an overview-oriented state of the art in the recent advancement in control strategies of AC/DC MG and its associated power converters control. Based on recent research, this article summarizes unobstructed views on different topologies, types of power converters, power converter controls, and control strategies of AC/DC MG. Finally, it identified some future challenges that need to be addressed in order to develop a sustainable and reliable control strategy for AC/DC MG. This article will serve as a guide for researchers.

Hydrothermal scheduling in the continuous-time framework

Continuous-time optimization models have successfully been used to capture the impact of ramping limitations in power systems. In this paper, the continuous-time framework is adapted to model flexible hydropower resources interacting with slow-ramping thermal generators to minimize the hydrothermal system cost of operation. To accurately represent the non-linear hydropower production function with forbidden production zones, binary variables must be used when linearizing the discharge variables and the continuity constraints on individual hydropower units must be relaxed. To demonstrate the performance of the proposed continuous-time hydrothermal model, a small-scale case study of a hydropower area connected to a thermal area through a controllable high-voltage direct current (HVDC) cable is presented. Results show how the flexibility of the hydropower can reduce the need for ramping by thermal units triggered by intermittent renewable power generation. A reduction of 34% of the structural imbalances in the system is achieved by using the continuous-time model.

Solar power forecast for a residential smart microgrid based on numerical weather predictions using artificial intelligence methods

Solar power forecast is a much needed means for grid operators, particularly in residential microgrids, to manage the produced energy in a dispatchable fashion. Deterministic methods are unable to accurately forecast the intermittent solar power generation since they depend on unique sets of inputs and outputs. Therefore, stochastic methods and artificially intelligent (AI) strategies are utilized for solar power forecast. In this work, a neural network (NN)-based numerical weather prediction (NWP) model is developed for a residential microgrid in San Diego, California considering all key weather parameters such as cloud coverage, dew point, solar zenith angle, precipitation, humidity, temperature, and pressure in the year 2016. The developed weather model is then used to predict the generated power in the residential smart microgrid. To validate the accuracy of the model, the solar irradiance and generated solar power in the residential microgrid are predicted for the year 2017 using the obtained NN-based model. The results are compared with the actual solar irradiance and power in 2017 to evaluate and validate the accuracy of the developed model. Furthermore, to showcase the effectiveness of neural networks in forecasting solar power and the accuracy of the NN-based model, the results are compared with those of two other methods including multi-variable regression (MVR) and support vector machine (SVM) approaches using mean absolute percentage error (MAPE) and mean squared error (MSE) criteria.

Small‐scale experimental study on the optimisation of a rooftop rainwater energy harvester using electromagnetic generators in light rains

This research proposes a new rooftop rainwater energy harvester using electromagnetic generators, which demonstrates better rain-energy harvesting performance than the traditional rainwater energy harvesters that use electromagnetic generators under light rain conditions. Conventionally, the rainwater collected from the roof of a building or a residential attic using the installed roof gutter system is drained directly along a pipe to drive water turbines combined with electromagnetic generators for generating electricity. Such an energy harvesting method has high energy harvesting efficiency under heavy rain conditions but is inefficient under light rain conditions. To improve the energy harvesting efficiency in light rains, a pipe-connected tank including a passive rainwater buffer is introduced and fixed below the rooftop gutters in the proposed structure. The passive rainwater buffer is attached above the pipe-connected tank. After collecting a certain amount of rainwater from the gutters, the passive rainwater buffer automatically releases the rainwater periodically into the pipe-connected tanks. The released rainwater results in a large flow along the pipe, which can drive water turbines with electromagnetic generators to work at a high efficiency for a short period of time. A small-scale experimental prototype was built for validating the energy harvesting performance of the proposed structure. The results show that the intermittent high-efficiency power generation of the proposed structure is better than the continuous low-efficiency power generation of the traditional structure during light rains. The proposed structure can charge a 680 μF capacitor with at least nine times the electrical energy that is stored in a conventional capacitor for a rainwater flow rate of less than 300 mL/min in the same amount of time. However, with the increase in the rainwater flow, the energy harvesting performance gap between the proposed structure and the traditional one gradually narrows. Highlights A new rooftop rainwater electromagnetic energy harvester is proposed; A pipe-connected tank including a passive rainwater buffer is fixed below the rooftop gutters; The proposed structure can charge a capacitor with at least nine times as much electrical energy as the conventional one in light rain conditions.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control (AGC). Thus, there is an urgent demand to coordinate AGC units with active equipment such as energy storage. Current dispatch decision-making methods often ignore the intermittent effects of renewable energy. This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units. This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units, improve the adjustment potential, and respond to the problems of intermittent power generation from renewable energy. A column and constraint generation algorithm is used to solve the model. In an example analysis, the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency

A state-of-the-art review on wind power deterministic prediction

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

Robust optimization for optimal day-ahead economic dispatch of CCHP considering wind-load uncertainty

Distributed wind power generators (WPGs) have been increasingly deployed into power system through combined cooling, heat, and power (CCHP) system. However, uncertain load and intermittent wind power generation adversely affect the economy of the system operation. In order to improve the economy of the CCHP operation, this paper presents a two-stage robust optimization model for the CCHP day-ahead (DA) economic dispatch considering the uncertainties of wind power generation and electric load demand. The model aims at minimizing the total operation cost. The day-ahead first stage is to decide the state and base generation of controllable generators that can withstand the worst-case scenario of the uncertainty while the real-time (RT) second stage is to adjust the generation of the generators according to the actual scenario of the uncertainty. To solve the model, duality theory, Big-M method, and column-and-constraint generation (C&CG) decomposition approach are employed to convert the model into tractable master problem and sub problem, so that a column-and-constraint generation iteration algorithm can be applied to figure out the optimal dispatch solution. Finally, experimental results reveal the effectiveness of the presented model and the employed method.