Load Uncertainty Research Articles

A multiple-objective capacity allocation optimisation method for cogeneration integrated energy systems considering life cycle carbon emission

ABSTRACT With the increasing penetration of renewable sources, the cogeneration integrated energy system (CIES) is much more efficient and flexible to achieve various types of energy supply for remote loads. Due to uncertainties in both sources and loads, the CIES should be carefully planned and optimised to keep it working at the suitable operation condition. Therefore, this paper proposes a multi-objective capacity allocation optimisation method for the islanded CIES, which is composed of renewable energy generation unit, electric-thermal conversion unit, electric storage, thermal storage and various loads. Firstly, three optimisation objectives are selected as typical indexes, including economic cost, solar energy utilisation, and life cycle carbon emission. An improved radar chart evaluation (RCE) solution is subsequently utilised for calculating a fitness function and building the multi-objective optimisation model. Besides, several compulsory constraints are supplemented to ensure the overall CIES performance satisfy energy supply requirement. As a mature and widely accepted algorithm, a genetic algorithm is chosen as an example to obtain the optimal capacity allocation scheme. Case study of an islanded CIES is provided on the basis of real data from the remote region of northwestern China, which successfully verifies the effectiveness of the proposed optimal capacity allocation method.

Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable generation not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.

Stability Analysis of Equilibrium of DC Microgrid Under Distributed Control

Constant power loads (CPLs) often lead to system instability and voltage collapse. This paper analyzes the existence and stability of equilibrium of meshed DC microgrids with multiple CPLs under distributed control, which aims to realize current sharing and voltage regulation. Firstly, the power-flow equation of the system is derived. To analyze the solvability of power-flow equation, we transform the problem of the multidimensional quadratic equation solvability into the existence of a fixed-point for a contraction mapping. Then, the sufficient solvability condition is derived based on Banach's fixed-point theorem. Secondly, we build the small-signal model to determine the system qualitative behavior around equilibrium. By analyzing the eigenvalue of system Jacobian matrix based on inertia theorem, the analytical stability conditions are obtained. Further, under the load uncertainty, a robust stability condition which only depends on the maximum load information instead of the real-time load information is derived. Simulation results verify the feasibility of the proposed theorems.

Retracted: Resiliency Enhancement of Distribution Power Grids Using Mobile Marine Power Source

In this paper, a new configuration security framework is developed for reliability and resiliency improvement of distribution networks by the coordination of the security-constraint unit commitment (SCUC) and mobile marine power sources (MMPSs) under both post-disaster and normal restoration operations. It is assumed that a MMPS contains non-dispatchable distributed generators (DGs), e.g., photovoltaics (PV), as well as dispatchable DGs, e.g., gas turbines and diesel generators. A mixed-integer linear programming model is formulated for coordinating MMPSs and SCUC under both normal and extreme conditions (e.g., natural disasters, cyber and physical attacks). To better characterize the uncertainties in renewable generation and electric load, a deep learning gated recurrent unit is adopted to forecast the PV power output. The proposed model is tested on the IEEE 69-bus distribution network to validate the effectiveness and merits of MMPSs. Results show that reliability and resiliency could be enhanced by using MMPSs within the network.

Multi-objective cooperative optimization of communication base station and active distribution grid under dual carbon targets

To achieve “carbon peaking” and “carbon neutralization”, access to large-scale 5G communication base stations brings new challenges to the optimal operation of new power systems, but also provides new opportunities for the low-carbon development of distribution networks. This paper develops a method to consider the multi-objective cooperative optimization operation of 5G communication base stations and Active Distribution Network (ADN) and constructs a description model for the operational flexibility of 5G communication base stations. Based on this, a multi-objective cooperative optimization 5G communication base station operating model and active distribution network considering the system operation economy and minimum carbon emissions as the optimization objectives are established. In the above model, by encouraging 5G communication base stations to engage in Demand Response (DR), the Renewable Energy Sources (RES), and 5G communication base stations in ADN are concurrently scheduled, and the uncertainty of RES and communication load is described by using interval optimization method. Finally, the problem is solved by combining the equivalent transformation based on interval analysis and the non-dominated sorting genetic algorithm. The analysis results of the example show that participation in grid-side dispatching through the flexible response capability of 5G communication base stations can enhance the power system’s renewable energy consumption and usage efficiency, resulting in significant low-carbon benefits.

Explicit Uncertainty Quantification for Probabilistic Assessment of Rotorcraft Handling Qualities

Early-stage vehicle design processes are typically subject to significant uncertainty in aerodynamic loads and control responses. In many cases, it may be necessary to consider this uncertainty in preliminary evaluations of stability, handling qualities, and performance metrics. This article introduces a novel methodology for computing these quantities in a probabilistic framework using the Koopman operator. The algorithm discretizes the uncertainty space and uses relevant transformations or simulations to map each point to the handling qualities evaluation domain, resulting in a stability, handling quality, and/or performance assessment for each discretized point. The expected value of the stability, handling quality, and/or performance requirement is then computed via quadrature integration, yielding a probabilistic assessment with respect to relevant specifications. The methodology is demonstrated through two examples involving a quadrotor unmanned aerial system and a generic utility helicopter. In the first example, the methodology is applied to evaluate the handling qualities of a quadrotor, for which a multiloop dynamic inversion controller is developed. In the second example, the method is used to quantify the impact of parametric uncertainty in a pilot model on specific pilot–vehicle system performance specifications and to predict vehicle handling qualities. The proposed explicit uncertainty quantification approach is shown to provide a convenient framework for the propagation of parametric uncertainty to stability, handling quality, and performance specifications with unique advantages over Monte Carlo techniques.

Probabilistic inspection planning for fatigue cracks in floating production, storage, and offloading units

Probabilistic inspection planning for fatigue cracks in floating production, storage, and offloading units

Dual-layer operation strategy of integrated energy system based on multivariate predictive control

Scheduling and control are two key dimensions to ensure the smooth operation of the integrated energy system, but there still exists the problem of insufficient accuracy of scheduling instructions due to modeling deviation between the scheduling model and the actual equipment. In this paper, a dual-layer operation strategy for integrated energy system based on multivariate predictive control is proposed, so as to make real-time corrections to the scheduling power instructions. The results show that the dual-layer operation strategy based on multivariate predictive control can better deal with the uncertainties of renewable energy output and electric load, and effectively reduce the impact of prediction deviation and scheduling model deviation on system operation. In the example calculation, compared with the operation strategy with only the scheduling layer, the total supply energy deviation with the dual-layer operation strategy decreases from 61.16 kWh to 22.96 kWh, which realizes the smooth and efficient operation of the system.

Robust compensation with adaptive fuzzy Hermite neural networks in synchronous reluctance motors

In this paper, a robust compensation scheme using adaptive fuzzy Hermite neural networks (RCAFHNN), for use in synchronous reluctance motors (SRMs), is proposed. SRMs have a simple underlying mathematical model and mechanical structure, but are affected by problems related to parameter variations, external interference, and nonlinear dynamics. In many fields, precise control of motors is required. Although the use of neural network and fuzzy are widespread, such controllers are affected by unbound nonlinear system model. In this study, RCAFHNN, based on an adaptive neural fuzzy interface system (ANFIS), was used to bound motor system model controller algorithm. RCAFHNN can be characterized in three parts. First, RCAFHNN offers fuzzy expert knowledge, a neural network for online estimation, and recursive weight estimation. Second, the replacement of the Gaussian function by the Hermite polynomial in RCAFHNN enables reduced membership function training times. Third, the system convergence and robustness compensation of RCAFHNN were confirmed using Lyapunov stability. RCAFHNN ameliorates the problems of external load and system lump uncertainty. The experimental results, in which the output responses of RCAFHNN and ANFIS (adaptive neural fuzzy interface systems) were compared, demonstrated that RCAFHNN exhibited superior performance.

Marginal Pricing in Adaptive Robust Unit Commitment Under Load and Capacity Uncertainty

Marginal Pricing in Adaptive Robust Unit Commitment Under Load and Capacity Uncertainty

Optimizing Power Supply Scheduling for Offshore Stand‐Alone Microgrids: A Novel Framework considering Load and Fuel Procurement Uncertainties

Optimizing Power Supply Scheduling for Offshore Stand‐Alone Microgrids: A Novel Framework considering Load and Fuel Procurement Uncertainties

Interval Holomorphic Embedding Load Flow Method: A novel approach for interval analysis considering load and generation uncertainties

Interval Holomorphic Embedding Load Flow Method: A novel approach for interval analysis considering load and generation uncertainties

Innovative Type II Fuzzy‐Based Voltage–Frequency Controller for Isolated Microgrid Considering Load Shedding and Uncertainties

Innovative Type II Fuzzy‐Based Voltage–Frequency Controller for Isolated Microgrid Considering Load Shedding and Uncertainties

Design of a Lean Distribution Model in the Electric Power Industry with a World-Class Approach

Objective: This study aims to design a lean distribution model tailored for the electric power distribution network in Tehran, integrating global best practices and expert insights to address the specific challenges and opportunities within this sector. Methodology: The study employed a mixed-method approach, combining qualitative and quantitative techniques. A comprehensive literature review was conducted to identify key factors influencing lean distribution. Expert interviews with managers and industry specialists in Tehran's electric power distribution sector provided additional insights. The factors were structured and categorized using Interpretive Structural Modeling (ISM) and MicMac analysis to establish their interrelationships and influence. Findings: The study identified 16 critical factors influencing lean distribution, with "World-Class Lean Distribution in the Electric Power Industry" emerging as the most influential. Other key factors included "Optimal Distribution of Reactive Power in Power Systems," "Estimation of Load Uncertainty," "Reduction of Loss Levels," and "Use of Fixed and Switchable Capacitors." The findings highlight the importance of infrastructure, ISO 9000 implementation, technological integration, internal capacity, and resilience. Conclusion: The research provides a comprehensive framework for implementing lean distribution practices in the electric power industry, emphasizing the need for global standards, technological advancements, and resilience. The findings suggest that adopting these practices can lead to improved reliability, reduced operational costs, and enhanced customer satisfaction, contributing to sustainable development and energy security.

An Open Source Stochastic Unit Commitment Tool using the PyPSA-Framework

This paper presents an open source stochastic unit commitment (UC) optimization tool, which is available on GitHub. In addition, it presents an example use case in which UC optimization is done for a waste-to-energy plant with thermal storage and a battery energy storage system (BESS) in Germany, under uncertain day-ahead and balancing power (aFRR) market prices as well as heat load uncertainty. The tool consists of multiple modular extensions for the Python for Power System Analysis (PyPSA) framework, namely the implementation of market and bidding mechanisms, stochastic optimization and multistaging.

Data driven net load uncertainty quantification for cloud energy storage management in residential microgrid

Data driven net load uncertainty quantification for cloud energy storage management in residential microgrid

A distributed peer‐to‐peer energy trading model in integrated electric–thermal system

AbstractTo promote the energy accommodation of both electrical and heating power while considering the source–load uncertainties, a peer‐to‐peer (P2P) energy trading model among prosumers in the integrated electric–thermal system is proposed here, considering the energy trading agent (ETA) with self‐build energy system. The solar heat‐pump hybrid thermal water system (SPTS) and the transferring loss of heating power is considered and modelled based on the principle of steady‐state thermal transfer. Since the variations of load and PV cannot be described by any single common distribution, the chance‐constraints programming based on the Gaussian mixture model (GMM) is proposed to handle the uncertainty of the net load. As the proposed power trading problem is a non‐convex problem with binary variables, an improved alternating direction method of multiplier (ADMM) based on the predictor‐corrector and two‐stage cycle iterations is proposed to enhance the convergence performance of standard ADMM. Finally, an example simulation verifies the effectiveness; this results show that the proposed model can decrease the total cost by 26.7% and enhance local energy balance by 61.8% compared to other cases.

Battery capacity design and optimal operation control of photovoltaic-battery system considering electrochemical aging

In recent years, the distributed photovoltaic battery (PVB) system is developing rapidly. To fully utilize photovoltaic production and increase the penetration of renewable energy, battery storage in distributed photovoltaic systems becomes essential. Despite plenty of studies dedicated to the capacity design and system control strategies under different work conditions, few research pay attention to the sophisticated battery storage aging problem and its effect on the system performance. This paper develops a five-parameter photovoltaic model and the electrochemical lithium battery model for the PVB system considering the residential load uncertainty in the distributed photovoltaic system. The battery and system performance under different capacity design and operation strategies are discussed. The results show that the oversize of the battery capacity design contributes to the capacity loss, leading to the increasement of levelized cost of storage, and the capacity design of 6, 8, 10 kWh under 100 %, 80 %, 70 % state of charge (SOC) charging limit is recommended in this case. Under the 100 % SOC limit, the battery capacity declines by 22 %–28 % annually, which is much higher than the limit of 80 % and 70 %. Meanwhile, the battery lifetime based on recommended capacity design can be improved further by 8.6 %, 4.0 % and 2.0 % with controlling the battery charge rate. Besides, a comparison between the electrochemical and traditional battery models is conducted, demonstrating that both models present a high consistency in system energy flow, while the traditional battery model lacks the internal electrochemical aging mechanism. The electrochemical battery model gives out a more precise battery aging result and reveals the different performances under varied control strategies.

Observer-Based Nonlinear Proportional–Integral–Integral Speed Control for Servo Drive Applications via Order Reduction Technique

This study designs an advanced single-loop output feedback system for speed servo drive applications, in which a simple proportional–integral–integral (PII) controller equipped with nonlinear feedback and feed-forward gains is formed. The resultant feedback system shows the desired critically damped performance for wide-operating regions by actively handling the system parameter and load uncertainties. There are three contributions: first, the third-order observer estimates, independent from the system model, where the speed and acceleration are obtained using the position measurement with the order reduction property; second, the observer-based PII controller is compensated by active damping with a nonlinearly structured feedback and feed-forward gains; and, third, a guarantee is achieved on the desired critically damped performance through a closed-loop analysis. A hardware testbed that adopts a 500 W brushless DC motor is used to experimentally demonstrate performance improvements over certain constant torque regions under various scenarios.

Deterministic and stochastic flexural behaviors of laminated composite thin-walled I-beams using a sinusoidal higher-order shear deformation theory

In this study, a novel sinusoidal higher-order shear deformation thin-walled beam theory is presented to examine the effects of material properties and external load uncertainty on static responses of laminated composite thin-walled beams with open sections. The solution for the deterministic flexural analysis is based on Hamilton’s principle and Ritz-type exponential shape function series. Several mechanical parameters of laminated composite materials are randomized and plugged into the beam solver to investigate the thin-walled beam’s stochastic flexural behaviors. The computational cost and accuracy of the polynomial chaos expansion (PCE) method with both projection and linear regression approaches are presented and evaluated by comparing its results with crude Monte Carlo simulation (MCS). This comparison allows for a thorough assessment of the PCE method’s performance. Additionally, a sensitivity analysis is conducted to compare the relative significance of the uncertainty in material properties and loads on the stochastic responses. The supervised training of the artificial neural network based on the MCS beam data is also conducted and compared to the PCE and MCS methods. The findings about the stochastic outputs are introduced in various statistical metrics and illustrations to demonstrate the influences of material properties’ randomness on different laminated composite thin-walled beam configurations.