Conventional Thermal Generators Research Articles

Stranded Asset Impairment Estimates of Thermal Power Companies Under Low-Carbon Transition Scenarios

The aspiration to reach the net zero carbon target has initiated new ideas for the sustainable development of the world economy. However, it has also accelerated the formation of stranded assets in high-carbon-emitting companies. Taking a Chinese thermal power company as an example, this paper proposes a model to estimate the degree of impairment loss for thermal power companies by integrating the net present value model with forward-looking carbon emission pathways under different policy intervention scenarios. The results show that under the low-carbon transition scenario with different policy interventions, the percentage of impairment loss of thermal power companies reaches up to 64.09%. Furthermore, impairment losses formed by stranded assets in the thermal power sector impose a severe shock on the national economy, as most of the impairment losses will ultimately be borne by the state treasury. Compared with conventional thermal power generation, new-energy power generation has a weak performance in delaying company bankruptcy caused by stranded assets. Therefore, in the process of a low-carbon transition, governmental departments should focus on the impairment loss of thermal power companies caused by stranded assets and should further integrate “green support” and “brown punishment” policies to effectively promote the low-carbon transition of thermal power companies.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Impact of renewables on the Peruvian electricity system

Peru is committed by international agreements such as the Paris Agreement and the UN 2030 Agenda for Sustainable Development to reduce its Green House Gas (GHG) emissions. Although Peru began promoting power plant projects based on renewable energies in 2008, the institutional impulse seems to have ceased today. Nonetheless, adopting renewable energy sources not only aids in fulfilling these commitments but also reduces electricity prices, fostering a more competitive economy. This study aims to provide a thorough analysis and evaluation of the impact that integrating Non-Conventional Renewable Energy Resources (NCRER) has on Peru's wholesale electricity market. Specifically, it focuses on the net total savings for the system, calculated as the difference between the reduction in energy costs traded and the cost of the renewable premium. To reach that goal a simplified yet analytical tool has been developed based on a multivariable regression of the real data of the market. The volume of natural gas not consumed and the volume of GHG emissions avoided will also be evaluated. The results show that based on the 2021 scenario, a NCRER share of 28.26 % would lead to a net saving for the final users of 316.04 MUSD in the annual energy traded in the market. The reduction of conventional thermal generation prevents the annual burning of 2.04 normal km3 of natural gas and the emission of 4.99 million tons of CO2-eq. To the authors' knowledge, this is the first time that the impact of NCRER production in the Peruvian electricity system has been quantitatively evaluated.

Robust economic–environmental dispatch problem for BESS and PV generators in MT-HVDC systems: A MI-MPC approach

The research addresses the energy management problem (EMP) in multi-terminal high-voltage direct current (MT-HVDC) systems using a mixed-integer model predictive control (MI-MPC). The MT-HVDC system consists of conventional thermal generation plants (fossil sources), renewable generation sources (solar photovoltaic plants), and battery energy storage systems (BESS). This combination creates a complex nonlinear EMP, as it introduces the power losses of the transmission networks through B-coefficients. The main contributions of this research can be summarized in three elements: (i) the formulation of a measurement- or simulation-based approach to determine B−coefficients in MT-HVDC systems for calculating power losses; (ii) the convexification of the EMP model through MI-MPC approaches, allowing to minimize economic and environmental objective functions; and (iii) the inclusion of uncertainties through the robust convex method. Numerical validations in the 11-bus grid, including the linear transportation model for the MT-HVDC system, confirm the multi-objective nature of the EMP when considering economic and environmental indices. In addition, the effect of uncertainties on generation and demand causes significant variations in the optimal Pareto front when compared to the deterministic scenario.

Mixed Integer Nonlinear Programming-Based Unit Commitment of Conventional Thermal Generators Using Hybrid Evolutionary Algorithms

Unit Commitment (UC) discusses the optimized generation resources (to turn on economical generators and turn off expensive generators),which are subjected to satisfy all the operational constraints. The operational constraints such as load balancing, security maximization, minimum up and down time, spinning reserve, and ramp up and down constraints are difficult to satisfy. Although, UC is a cost minimization problem that is realized by committing less expensive units while satisfying the corresponding constraints, and dispatching the committed units economically. The UC problem is an np-hard Mixed Integer Nonlinear Problem (MINLP). Therefore, in this paper, hybrid EA based on a Genetic Algorithm (GA) has been applied to find the optimal solution to the UC problem. Moreover, during the search process, it is very difficult to discard infeasible solutions in EAs. Hence, the Genetic Algorithm (GA) is integrated with the feasibility rule constraint handling technique to emphasize feasible solutions. IEEE RTS Eleven Thermal Generator Standard Test system is used to validate the performance of proposed methods. For the validation and the superiority of the proposed algorithm, simulation results are compared with the classical Lagrangian Relaxation (LR) methods. Results show that the proposed method can find the global optimal solution to the UC problem which is subjected to satisfy all the operational constraints.

Modeling forecast errors for microgrid operation using Gaussian process regression

Microgrids, denoting small-scale and self-sustaining grids, constitute a pivotal component in future power systems with a high penetration of renewable generators. The inherent uncertainty tied to renewable power generation, typified by photovoltaic and wind turbine systems, necessitates counterbalancing mechanisms. These mechanisms encompass Energy storage systems or conventional thermal fossil-fuel generators imbued with heightened flexibility. Addressing the uncertainty stemming from renewable generators mandates a cost-effective assessment and operational strategy for said compensatory devices. To this end, myriad uncertainty factors warrant scrutiny, conceivably concretized into a unified probability distribution function (PDF) that takes into account their temporal inter-dependencies. Diverse uncertainty factors, characterized by varying marginal distributions and scales, can be assimilated into a multivariate probability distribution through a conversion to normal distributions via rank correlation. However, with the escalation in the number of uncertainty factors embraced within a microgrid context, the endeavour becomes notably intricate when aiming to define conditional probability distributions originating from joint PDFs. This paper presents a method proposing the modelling of net-load forecast error distribution, considering the interplay among uncertainty factors. The approach introduces a data-driven Gaussian process regression technique for training and validating conditional PDFs among these uncertainty factors. Notably, this approach facilitates the transformation of said factors into normal distributions while preserving their inherent marginal characteristics. The resultant conditional density function, as per the proposed methodology, exhibits enhanced suitability for estimating net-load error distribution. Consequently, the conditional density function stemming from this proposed approach demonstrates superior aptitude in approximating the distribution of net load error.

Optimal site and size of FACTS devices with the integration of uncertain wind generation on a solution of stochastic multi-objective optimal power flow problem

To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally.

Dynamic Economic Dispatch Problem in Hybrid Wind Based Power Systems Using Quasi Oppositional Based Chaotic Honey Badger Algorithm

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods.

Multiobjective Economic/Environmental Dispatch Using Harris Hawks Optimization Algorithm

The eminence of Economic Dispatch (ED) in power systems is significantly high as it involves in scheduling the available power from various power plants with less cost by compensating equality and inequality constrictions. The emission of toxic gases from power plants leads to environmental imbalance and so it is highly mandatory to rectify this issues for obtaining optimal performance in the power systems. In this present study, the Economic and Emission Dispatch (EED) problems are resolved as multi objective Economic Dispatch problems by using Harris Hawk’s Optimization (HHO), which is capable enough to resolve the concerned issue in a wider range. In addition, the clustering approach is employed to maintain the size of the Pareto Optimal (PO) set during each iteration and fuzzy based approach is employed to extricate compromise solution from the Pareto front. To meet the equality constraint effectively, a new demand-based constraint handling mechanism is adopted. This paper also includes Wind energy conversion system (WECS) in EED problem. The conventional thermal generator cost is taken into account while considering the overall cost functions of wind energy like overestimated, underestimated and proportional costs. The quality of the non-dominated solution set is measured using quality metrics such as Set Spacing (SP) and Hyper-Volume (HV) and the solutions are compared with other conventional algorithms to prove its efficiency. The present study is validated with the outcomes of various literature papers.

Transient simulation of biomass combustion in a circulating fluidized bed riser

Interest in circulating fluidized bed (CFB) boilers as a power generation technology has sky-rocketed in recent years because of several advantages this technology offers over conventional boilers, such as increased gas-solid mixing, which results in higher combustion efficiency and the ability to use lower rank fuels. CFB combustors are operated at lower temperatures than conventional thermal power generation combustors, thus reducing NOx emissions, while SO2 emissions can be conveniently controlled through the addition of Ca-based sulfur sorbents within the combustor.This paper summarizes the modeling effort on a 50 kWth CFB combustor designed, built, and operated at CanmetENERGY in Ottawa, Canada. The numerical model employs the multiphase particle-in-cell (PIC) approach in the open-source Multiphase Flow with Interphase eXchanges (MFiX) Software Suite. The MFiX-PIC model parameters for the simulation are tuned against cold-flow experiments from CanmetENERGY using olivine sand as the inert bed material. It is shown that for the relatively coarse fluid meshes and large parcel sizes necessitated by the scale of the simulation, filter size dependent corrections to the drag law must be incorporated to ensure accuracy of the simulation results.The validated cold flow model is extended to simulate reacting flow with torrefied hardwood as the feedstock and to validate the combustion reaction scheme. The species concentrations at the riser outlet are compared against CanmetENERGY's experiments and show satisfactory agreement. The simulations demonstrate the ability of MFiX-PIC to accurately capture both the physics and chemistry of a CFB combustor at bench scales, which can be further extended to pilot- and industrial-scale systems.

Combined economic emission dispatch in hybrid power systems using competitive swarm optimization

In last few decades, the emission of greenhouse gasses has exponentially increased due to large production of electric power energy from conventional fossil fuels to pose critical environmental challenges. The renewable energies (REs) are establishing themselves as key technologies for reduction of carbon emissions, in addition to low cost and high efficiency. However, the operational limits and the power generation procedures of the renewable energies invite immense challenges. The uncertainty in production with precise and error free approximation make it very complicated. Hence, an effective approach with methodical organization of the renewable energies are the need of the hour for reliable and safe system. In this study, an IEEE 30-bus hybrid power system (HPS) problem consisting of conventional thermal generators and green energies like wind generators and solar photovoltaic are considered to become environmentally and economically capable than the existing ones. Several measures like penalty cost and reserve cost have been considered in this present study for addressing the uncertainty issues underestimation and overestimation respectively. Further, three hybrid configurations such as thermal-solar (TS), thermal-wind (TW) and thermal-wind-solar (TWS) are proposed to perform the cost effective analysis. The adopted hybrid power system is extremely complex and non-linear optimization problem. Hence, a recently proposed evolutionary algorithm namely competitive swarm optimization (CSO) algorithm is implemented to discover the optimum result for the variety goals like minimum production cost, carbon emission, voltage variation and loss of the power. The performance of CSO algorithm is compared with several state-of-the-art meta-heuristic algorithms such GA, PSO, CSA, ABC, and SHADE-SF. The extraordinary outcomes achieved in this work illustrate that the CSO method can successfully be applied to handle the complex, non-convex and non-linear hybrid power system problems.

Optimal Power Flow of Renewable-Integrated Power Systems Using a Gaussian Bare-Bones Levy-Flight Firefly Algorithm

This article proposes a Gaussian bare-bones Levy-flight firefly algorithm (GBLFA) and its modified version named MGBLFA for optimizing the various kinds of the different optimal power flow (OPF) problems in the presence of conventional thermal power generators and intermittent renewable energy resources such as solar photovoltaic (PV) and wind power (WE). Several objective functions, including fuel costs, emission, power loss, and voltage deviation, are considered in the OPF problem subject to economic, technical, and safety constraints. Also, the uncertainties of solar irradiance and wind speed are modeled using Weibull, lognormal probability distribution functions, and their influences are considered in the OPF problem. Proper cost functions associated with the power generation of PV and WE units are modeled. A comprehensive analysis of ten cases with various objectives on the IEEE 30-bus test system demonstrates the potential effects of renewable energies on the optimal scheduling of thermal power plants in a cost-emission-effective manner. Numerical results show the superiority of the proposed method over other state-of-the-art algorithms in finding optimal solutions for the OPF problems.

Comparative analysis of thermal economy of two SOFC-GT-ST triple hybrid power systems with carbon capture and LNG cold energy utilization

This paper studies the coupling of fuel cell power generation technology and conventional thermal power generation technology, constructs the thermodynamic model of combined cycle power generation of fuel cell, gas turbine and steam turbine, and analyzes the thermodynamic performance of the system from the important links such as compressor pressure ratio, steam carbon ratio, fuel utilization, fuel cell operating temperature and gas recirculation. Since natural gas as fuel will emit a lot of greenhouse gases, two new low-carbon power generation systems including composite power system, carbon capture and liquified natural gas cold energy utilization are proposed. In this paper, the post-combustion carbon capture which is more practical and conducive to the safe operation of the system is used. The adsorbents used in the two carbon capture methods are MDEA and CaO respectively. In the MDEA carbon capture process, flash tower and split flow process are added to reduce energy consumption. In the calcium looping carbon capture process, CO2 reflux process is set to optimize the calcination temperature, and two-stage waste heat boilers are set to recover a large amount of waste heat of CO2 and clean flue gas respectively. After that, to further save energy consumption, a method of cascade utilization of liquified natural gas cold energy is proposed. It can not only increase power output, but also provide natural gas at normal temperature and pressure for the system and the outside. The calculation results show that the efficiency of the optimized combined cycle system of fuel cell, gas turbine and steam turbine reaches 70.32%. In terms of energy saving, the system efficiency of MDEA carbon capture process is 1.81% higher than that of calcium looping carbon capture process, and calcium looping carbon capture process has more advantages in economic cost. In addition, the cascade utilization of liquified natural gas cold energy provides additional 410.86 kW, 382.64 kW electric energy and more directly usable natural gas for the two systems. The research results of this paper can provide meaningful guidance for the design of low-carbon and high-efficiency power generation system based on fuel cell.

An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

Техніко-економічні показники технологій теплової енергетики, що експлуатуються в маневрених режимах

There is a deficit in united energy system of Ukraine of installed capacity of flexible generation units which are able to quickly reach the operating mode, change the power generation levels in a wide range. To model additional measures increasing the flexibility of the power system, it is necessary to know the technical and economic indicators of thermal power technologies and its specific operating modes. The purpose of the article is to determine: efficiency factors depending on the power unit load; start-up costs depending on downtime duration and different fuel prices; the influence of maneuvering modes on lifetime resource indicators; as well as comparison of indicators for various technologies of thermal energy. The following thermal power technologies: natural gas, hard or bituminous coal fired steam turbine supercritical pressure, steam-gas, gas turbine open cycle and gas piston engines. The calculation formulas allowing estimate the influence of the power plant operation mode on the efficiency is obtained. The results of calculation of the start-up specific costs depending from the type of the unit and the idle time is presented. The different values of thermal power unit start-up cost are presented depending on idle time. As an example, the balancing of 660 MW electric power with different equipment composition are considered. The analysis of the results shows that the highest start-up cost is for coal-fired steam turbine plants, and the composition of the equipment with the lowest start-up cost varies depending on the downtime: up to 2 hours, 2 – 5 hours, more than 5 hours. Formulas for recalculating the cost of starting at different fuel prices are obtained. The influence of maneuvering modes on the lifetime resource indicators of thermal power plants is also presented. The conventional thermal power generators of the united energy system of Ukraine have actually overcome their physical capabilities to ensure effective load following, therefore, it is necessary to introduce new highly flexible capacities, and the proposed formulas and results could be helpful to determine the volumes and characteristics of new thermal power plants needed to implement into power system. Keywords: thermal power, maneuverable mode, efficiency, downtime, start-up cost, service life

Data centre day-ahead energy demand prediction and energy dispatch with solar PV integration

This paper presents a novel Marine Predators Algorithm for both training an Artificial Neural Network model used for predicting the energy demand and for solving a dynamic combined economic and emission dispatch of a data centre. The MPA is proposed for first finding the optimal weights and biases of the neural network based on a Mean Squared Error and Mean Absolute Error minimization objective function. Real life dataset obtained from an anonymous data centre operator in Cape Town, South Africa was used for the model implementation. The dataset was made up of a total of 564 samples and was split into training and testing set using an 80:20 ratio. The input variables contained in the dataset are the data centre’s ambient temperature, ambient relative humidity, chiller output temperature and Computer Room Air Conditioning supply temperature while the energy demand is the target variable. The optimal weights of the neural network model were analysed using a weights-based approach to determine the level of influence each input parameter of the model has on the data centre’s energy demand. Then based on the predicted energy demand of the data centre, a dynamic economic and emission dispatch problem is solved for the building while considering thermal and solar photovoltaic generations. A spinning reserve is also incorporated in the energy dispatch model to cater for any shortfall that may exist between the predicted and actual energy demand of the data centre due to possible inaccuracies in the energy demand prediction model. Results for the energy demand prediction task showed that the proposed method outperformed the state-of-the-art by producing the least Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and highest prediction accuracy for the training and testing sets. Further analyses also highlighted that the data centre’s ambient temperature has the highest influence of about 37.63% on its energy demand pattern. For the energy dispatch task, the proposed method also identified solar photovoltaic as the preferred energy source over conventional thermal generators in fulfilling the objective function, depending on its availability. Overall, the findings presented in this study emphasize the robustness of the proposed method in solving the problems considered and its potential application towards solving even more complex problems.

Stochastic Market Operation for Coordinated Transmission and Distribution Systems

This paper proposes a three-stage unit commitment model for the market operation of transmission and distribution coordination under the uncertainties of renewable generation and demand variations. The first stage is for the independent system operator (ISO) that determines the commitment decisions of the transmission-level generators and the distribution-level system reconfiguration; the second stage optimizes the transmission economic dispatch; then distribution system operators (DSOs) in the third stage perform their economic dispatch and exchange information with the ISO at the boundary nodes. Between the transmission and distribution networks, not only conventional thermal generators but renewables and variable demands are considered, which are tackled via a multi-stage stochastic programming approach. The model adopts a convexified AC branch flow formulation in the distribution system. We devise a generalized nested L-shaped algorithm to solve the proposed framework in an efficient manner. Numerical experiments on multi-scale test systems corroborate the efficacy of this strategy.

Hybrid Mean Variance Mapping Optimization Algorithm for Solving Stochastic Based Dynamic Economic Dispatch Incorporating Wind Power Uncertainty

This paper presents a model for studying the dynamic economic load dispatch problem in power system including conventional thermal generators and wind turbines. The problem is solved using the hybrid variant Mean Variance Mapping Optimization technique. Due to the stochastic nature of wind speed, the Weibull distribution parameters of wind speed are obtained for different time intervals (24 hours). Consequently, the probability density function for the output wind power is achieved for each hour separately. The constraints for the thermal generators are considered including generation limits, ramp rate limits, valve-point loading and spinning reserve. The optimization problem is solved for different scenarios and total operating cost obtained shows the effect of wind power penetration as well as the overestimate and underestimate power probabilities.