Violation Of Operating Limits Research Articles

Decoupled probabilistic feeder hosting capacity calculations using general polynomial chaos

This paper introduces a decoupled method of calculating the PV hosting capacity of low voltage distribution system (LVDS) feeders for new solar photovoltaics (PV) installations. The hosting capacity calculation of LVDS is a multidimensional stochastic problem. A general polynomial chaos-based probabilistic power flow is used to solve this problem, as it allows for fast computation times without any compromise in accuracy. Two types of uncertainties exist in the hosting capacity calculation problem: planning level uncertainties such as size, location, type, and number of PV installations and operational uncertainties such as consumer load and PV generation. These two types of uncertainties are usually sampled together in probabilistic hosting capacity approaches. In this paper, a decoupled approach is presented where the impact of planning level scenarios on the probability of violation of operational limits is studied. The highest total PV among the planning scenarios inside the probabilistic bound of the operational limit is termed as the hosting capacity of the feeder. The results show that hosting capacity depends upon the planning uncertainties and operational variables, and decoupling them gives a more concise picture of the impact of different uncertainties.

Intelligent Tools to Monitor, Control and Predict Wastewater Reclamation and Reuse.

Contemporary wastewater reclamation units entail several diverse treatment and extraction processes, with a multitude of monitored quality characteristics, controlled by a variety of key operational parameters directly affecting the efficiency of treatment. The conventional optimization of this highly complex system is time- and energy- consuming, frequently relying on intuitive decision making by operators, and does not predict or forecast efficiency changes and system maintenance. In this paper, we introduce intelligent solutions to enhance the operational control of the unit with minimal human intervention and to develop an AI-powered DSS that is installed atop the sensors of a water treatment module. The DSS uses an expert model, both to assess the quality of water and to offer suggestions based on current values and future trends. More specifically, the quality of the produced water was successfully visualized, assessed and rated, based on a set of input operational variables (pH, TOC for this case), while future values of monitored sensors were forecasted. Additionally, monitoring services of the DSS were able to identify unexpected events and to generate alerts in the case of observed violation of operational limits, as well as to implement changes (automatic responses) to operational parameters so as to reestablish normal operating conditions and to avoid such events in the future. Up to now, the DSS suggestion and forecasting services have proven to be adequately accurate. Though data are still being collected from early adopters, the solution is expected to provide a complete water treatment solution that can be adopted by a vast range of parties.

Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

As more renewables are integrated into the power system, capacity expansion planners need more advanced long-term decision-making tools to properly model short-term stochastic production uncertainty and to explore its effects on expansion decisions. We develop a distributionally robust generation expansion planning model, accounting for a family of potential probability distributions of wind forecast error uncertainty. Aiming to include more realistic distributions, we construct more informed moment-based ambiguity sets by adding structural information of unimodality. We include operational-stage unit commitment constraints and model the risk of operational limit violations in two distinct forms: chance and conditional value-at-risk (CVaR) constraints. In both forms, the resulting expansion planning model is a mixed-integer second-order cone program. Using a thorough out-of-sample numerical analysis, we conclude: (i) the distributionally robust chance-constrained generation expansion planning model exhibits a better out-of-sample performance only if sufficiently accurate information about the first- and the second-order moments as well as the mode location of potential distributions is available; (ii) conversely, if such accurate information is unavailable, the distributionally robust CVaR-constrained generation expansion planning model outperforms; (iii) these two models have a similar performance when unimodality information is excluded.

Direct method for transient stability assessment of a single wind turbine generator subject to LVRT requirements

The authors report on a step towards the development of direct methods for transient stability assessment of power systems with integrated wind turbine generators subject to low voltage ride-through constraints. In this study, they propose an approach to assess transient stability of a single wind turbine generator against an infinite bus using an energy function along with an extension of the potential energy boundary surface method. The proposed approach exploits the theory of constrained dynamical systems and the concept of constrained stability region to efficiently estimate critical clearing times taking into account not only the issue of stability due to rotor acceleration but also the issue of operational limit violations, such as low-voltage ride-through curve violations. Very often, wind turbine generators are disconnected due to violation of operational limits, such as under-voltage limits, instead of rotor acceleration. The proposed approach correctly estimates the critical clearing times to avoid operational limit violations, such as the low-voltage ride-through constraint violation, while traditional direct methods would fail to appropriately estimate such clearing times. They illustrate the effectiveness of the proposed approach by means of case studies considering two types of wind turbine generators, the fixed- and limited-variable-speed wind turbine generators.

Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filter coupled with a robust adaptive MPC

Electric submersible pumps (ESPs) are one of the most widespread oil artificial lifting technologies. In the operation of an ESP there are a large number of parameters that must be monitored and held within operational constraints in order to guarantee stable and optimal operation. Manual control is subject to sub-optimal production and constant violation of operational limits, that can cause either a reduction in ESP lifetime or premature failure. Therefore, a proper automation strategy must be applied to support operators in order to ensure the best production rate with less energy cost. Previous literature has proposed the use of linear MPC based on system identification, however all relevant system variable measurements were considered available. In this paper, the problem of losing measurements of the state variables due to the aggressive subsea environment is addressed. We show that a non-adaptive single linear model strategy lacks in quality for state estimation and, therefore, a robust MPC is not possible under this configuration. In this work, an adaptive constrained MPC coupled with a model scheduling Kalman filter (MSKF) is proposed. Two model scheduling strategies based on linear interpolation of a pre-set number of local models are proposed and compared to successive linearization at every sampling time, based on Taylor series expansion of the nonlinear model. All strategies guarantee model accuracy and model stability over the whole operational range. The proposed scheduling strategies presented similar performance compared to the successive linearization strategy, avoiding the need of obtaining a local linear model at each sampling time.

Optimised curtailment of distributed generators for the provision of congestion management services considering discrete controllability

Grid operation becomes a more challenging task due to the increasing amount of distributed generators in the system. Consequently, critical situations with violations of operational limits occur more frequently. Hence, grid operators have to take corrective countermeasures to ensure the security of supply. This study presents a discrete optimisation approach to determine the optimal amount of distributed generator curtailment regarding multiple objectives. The proposed method is based on non-linear AC load flow analysis of the network. The method is implemented into a modified version of the IEEE 14-bus test system and the necessary amount of generation curtailment to retain predefined operational limits is identified. Simulations are carried out for different contingency scenarios and several possible increments of curtailment levels are compared. Results reveal the potential to reduce the total amount of power adjustments distinctly by using curtailment levels with smaller step sizes compared to todays' applied standards in German grid operation. Furthermore, the developed methodology enables distributed generators to be used for managing congestions of transformers connecting different voltage levels.

Hosting Capacity Enhancement in Distribution System in Highly Trenchant Photo-Voltaic Environment: A Hardware in Loop Approach

The gradual fossil fuel depletion, the exponential increase in load, the environmental, social and political concern has lead to the gradual sprawl of distributed energy resources in the form of microgrid. But disproportionate integration of DERs into the conventional power system may result in the anomalous system behavior causing violation of operational limits i.e., under and overvoltage, thermal overloading, line losses, transformer or feeder overloading, harmonic distortion breaching the standards, mal-operation of the protective devices etc. The key to mitigate these violations is appropriate hosting capacity assessment. This paper proposes the concept of customary hosting capacity and enhanced hosting capacity at all nodes in a network with low voltage level, high voltage level, ampacity and total harmonic distortion as the governing criteria. The analysis is tested on reconfigured IEEE 33 bus distribution system, whose reconfiguration is achieved by particle swarm optimization, keeping loss minimization as the objective function. The attainment of enhanced hosting capacity is significant since it may lead to considerable improvement of voltage in the network, maintaining the line current values very low. The experimentation on reconfigured and base configuration of IEEE 33 bus distribution system is performed in MATLAB and validated in hardware in loop. The results are also compared with existing literature to prove its efficacy.

Stochastic Security Assessment for Power Systems With High Renewable Energy Penetration Considering Frequency Regulation

With the deepening penetration of renewable resources worldwide, power system operators are faced with emerging challenges, e.g., the increase of operating risks due to the volatility and uncertainty of wind and solar power. To efficiently identify the operational limit violations, a switch from deterministic to stochastic framework for assessing the system security, which could manage various types of uncertainties, has been advocated in this paper. The established model is based on an improved probabilistic load flow, which is adapted to incorporate the steady-state behavior of frequency regulation. An efficient importance sampling (IS) technique is also developed to speed up the crude Monte Carlo (MC) simulation in estimating the low probability of violations of security constraints. Extensive computational experiments on both the IEEE 14-bus test case and a simplified regional system show that the proposed IS estimator makes significant enhancement to the crude MC in the computational efficiency and has better numerical performance as compared with other IS schemes.

Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management

Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent constraints. To overcome these problems, this paper presents a time-horizon three-phase grid-supportive demand side management methodology for low voltage networks by using a universal interface that is established between the demand side management application and the monitoring and network analysis tools of the network operator. Using time-horizon predictions of the system states that the probability of operational limit violations is identified. Since this analysis is computationally intensive, a data driven approach is adopted by using machine learning. Time-horizon flexibility is procured, which effectively prevents operation limit violation from occurring independent of the objective that the demand side management application has. A practical example featuring fair power sharing demonstrates the effectiveness of the presented method for resolving over-voltages and under-voltages. This is followed by conclusions and recommendations for future work.

State-of-the-art of hosting capacity in modern power systems with distributed generation

Abstract Currently, renewable energy is rapidly developing across the world in response to technical, economic and environmental developments, as well as political and social initiatives. On the other hand, excessive penetration of distributed generation (DG) systems into electrical networks may lead to various problems and operational limit violations, such as over and under voltages, excessive line losses, overloading of transformers and feeders, protection failure and high harmonic distortion levels exceeding the limits of international standards. These problems occur when the system exceeds its hosting capacity (HC) limit. The HC is a transactive approach that provides a way for the distribution network to be integrated with different types of energy systems. Accordingly, HC assessment and enhancements become an essential target for both distribution system operators and DG investors. This paper provides, for the first time, a systematic and extensive overview of the HC research, developments, assessment techniques and enhancement technologies. The paper consists of four HC principal sections: historical developments, performance limits, perceptions and enhancement techniques. Besides, practical experiences of system operators, energy markets and outcomes gained from real case studies are presented and discussed. It was concluded that success in integrating more distributed generation hinges on accurate hosting capacity assessment.

Practical Considerations for Optimal Conductor Reinforcement and Hosting Capacity Enhancement in Radial Distribution Systems

The high penetration level of distributed generation (DG) units may lead to various problems and operational limit violations in electric power distribution systems if it exceeds a particular limit known as the system’s hosting capacity (HC). In this paper, the problem of selecting the optimal conductor for a real radial distribution system in Egypt is investigated using a recent meta-heuristic algorithm, known as salp swarm optimization. First, a constrained optimization problem is introduced to minimize the combined annual cost of energy losses and the investment cost of the conductors while complying with the system voltage limits and conductor thermal capacities. The results obtained show the effectiveness of the algorithm in satisfying the objective function and constraints. However, the optimization results also show that a reduction in the size of some existing conductors should take place, although this is not allowed by the utilities because of practical reasons such as load growth, variations in loading scenarios, and the possibility of connecting DG units with uncertain penetration levels and locations. Hence, a practical feeder reinforcement approach is proposed to maintain the constraints while considering these uncertainties. Further, a novel feeder reinforcement index is proposed to assist the distribution system operators and planners to determine the feeders that first need to be reinforced. The results obtained show that the proposed reinforcement approach attains a better level of HC than can be obtained with the conventional conductor selection approach under the same testing conditions.

Analysis of Series-DC Offshore Wind Plants with Aerodynamic Wake Effects

In series-dc wind plants, variations in power output among turbines due to aerodynamic effects may lead to violations of operational limits of electric machines and power electronic converters. Turbine wakes constitute the predominant cause of such variations. It is, therefore, imperative to incorporate wake models during analysis of wind plant-level issues and control strategies. In this paper, two candidate energy conversion configurations previously proposed for series-dc connected turbines are considered. They are analyzed in the context of a notional offshore wind plant using the Jensen wake model. An optimization problem is formulated for maximizing total plant power output. A key finding is that this objective is attainable without any turbine violating operational limits for arbitrary wind direction and speed. This, however, may require variation of transmission voltage or current as a function of prevailing wind conditions by means of the onshore converter interfacing the plant with the terrestrial power grid.

Risk Assessment for Power System Operation Planning With High Wind Power Penetration

Integration of wind power generation (WPG) is increasing rapidly worldwide. The variability and uncertainty of wind energy may lead to significant load-generation imbalances resulting in large frequency deviations, hence increasing system operational risks, especially in small and isolated power systems with low inertia and limited capabilities of providing frequency responses. This raises the need for investigating alternatives to current power system operation planning approaches to cope with the uncertain nature of the intermittent generation. This paper presents a risk assessment approach to analyze power system security for operation planning under high penetration of wind power generation. The proposed approach deals with not only steady-state voltage and overload evaluations, but also frequency response adequacy. For fast identification of operational limit violations in the proposed risk assessment method, we develop an analytical procedure for approximating frequency response and assessing the consequences of limit violations without performing dynamic simulations. As a result, the frequency response adequacy assessment can be run simultaneously with the steady-state voltage and overload evaluations. The proposed risk assessment approach is illustrated via its application to a model of a power system with high wind power penetration.

Control of the tokamak safety factor profile with time-varying constraints using MPC

A controller is designed for the tokamak safety factor profile that takes real-time-varying operational and physics limits into account. This so-called model predictive controller (MPC) employs a prediction model in order to compute optimal control inputs that satisfy the given limits. The use of linearized models around a reference trajectory results in a quadratic programming problem that can easily be solved online. The performance of the controller is analysed in a set of ITER L-mode scenarios simulated with the non-linear plasma transport code RAPTOR. It is shown that the controller can reduce the tracking error due to an overestimation or underestimation of the modelled transport, while making a trade-off between residual error and amount of controller action. It is also shown that the controller can account for a sudden decrease in the available actuator power, while providing warnings ahead of time about expected violations of operational and physics limits. This controller can be extended and implemented in existing tokamaks in the near future.

Distribution network reliability improvements in presence of demand response

Demand response (DR) as a key integral part of the future smart grid is gaining a great and still growing focus of attention in nowadays electric power industries. However, many potential benefits of DR, although they have been envisioned to be significant, have not been yet thoroughly and quantitatively investigated. DR provides network operators with the opportunity to mitigate operational limit violations by load modification in place of load shedding when network reliability is jeopardised. This paper aims to comprehensively assess the potential impacts of DR on major attributes of service reliability in a residential distribution network. For doing so, firstly, load profiles for major residential appliances are extracted from grossly metered consumptions. Secondly, the flexibility associated with individual load profiles is estimated using the statistical data gathered through surveys and questionnaires. Thirdly, for every contingency, appliance level load profiles are modified based on their flexibilities such that the least possible interruption cost is realised. The obtained results are finally combined to calculate service reliability indices. The proposed framework is applied to Finnish distribution system and the obtained results demonstrate the efficiency and applicability of the proposed approach in real-world systems.

Reactive optimal power flow incorporating margin enhancement constraints with nonlinear interior point method

A reactive optimal power flow incorporating margin enhancement constraints is described. Margin sensitivity at a steady-state voltage instability point is calculated using invariant space parametric sensitivity, and can provide valuable information for selection of effective control parameters. However, weakest buses in neighbouring regions have high margin sensitivities in a certain range, so control determination using only the sensitivity information might cause violation of operational limits of the base operating point, at which the control is applied to enhance voltage stability margin in the direction of parameter increase. A nonlinear interior point method (NIPM) is applied to solve the optimal power flow formulation with margin enhancement constraints, and shunt capacitances are mainly considered as control variables. In the case study, an example applying the proposed method to the IEEE 118-bus test system is shown, and experiences with margin enhancement constraints are also included.