Load Balancing with Energy Storage Systems Based on Co-Simulation of Multiple Smart Buildings and Distribution Networks

In this paper, we present a co-simulation framework that combines two main simulation tools, one that provides detailed multiple building energy simulation ability with Energy-Plus being the core engine, and the other one that is a distribution level simulator, Matpower. Such a framework can be used to develop and study district level optimization techniques that exploit the interaction between a smart electric grid and buildings as well as the interaction between buildings themselves to achieve energy and cost savings and better energy management beyond what one can achieve through techniques applied at the building level only. We propose a heuristic algorithm to do load balancing in distribution networks affected by service restoration activities. Balancing is achieved through the use of utility directed usage of battery energy storage systems (BESS). This is achieved through demand response (DR) type signals that the utility communicates to individual buildings. We report simulation results on two test cases constructed with a 9-bus distribution network and a 57-bus distribution network, respectively. We apply the proposed balancing heuristic and show how energy storage systems can be used for temporary relief of impacted networks

Bulk electric system reliability simulation and application

Bulk electric system reliability analysis is an important activity in both vertically integrated and unbundled electric power utilities. Competition and uncertainty in the new deregulated electric utility industry are serious concerns. New planning criteria with broader engineering consideration of transmission access and consistent risk assessment must be explicitly addressed. Modern developments in high speed computation facilities now permit the realistic utilization of sequential Monte Carlo simulation technique in practical bulk electric system reliability assessment resulting in a more complete understanding of bulk electric system risks and associated uncertainties. Two significant advantages when utilizing sequential simulation are the ability to obtain accurate frequency and duration indices, and the opportunity to synthesize reliability index probability distributions which describe the annual index variability. This research work introduces the concept of applying reliability index probability distributions to assess bulk electric system risk. Bulk electric system reliability performance index probability distributions are used as integral elements in a performance based regulation (PBR) mechanism. An appreciation of the annual variability of the reliability performance indices can assist power engineers and risk managers to manage and control future potential risks under a PBR reward/penalty structure. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the “system well-being” of bulk electric systems and to evaluate the likelihood, not only of entering a complete failure state, but also the likelihood of being very close to trouble. The system well-being concept presented in this thesis is a probabilistic framework that incorporates the accepted deterministic N-1 security criterion, and provides valuable information on what the degree of the system vulnerability might be under a particular system condition using a quantitative interpretation of the degree of system security and insecurity. An overall reliability analysis framework considering both adequacy and security perspectives is proposed using system well-being analysis and traditional adequacy assessment. The system planning process using combined adequacy and security considerations offers an additional reliability-based dimension. Sequential Monte Carlo simulation is also ideally suited to the analysis of intermittent generating resources such as wind energy conversion systems (WECS) as its framework can incorporate the chronological characteristics of wind. The reliability impacts of wind power in a bulk electric system are examined in this thesis. Transmission reinforcement planning associated with large-scale WECS and the utilization of reliability cost/worth analysis in the examination of reinforcement alternatives are also illustrated

Using probability density functions to analyze the effect of external threats on the reliability of a South African power grid

Includes bibliographical references.The implications of reliability based decisions are a vital component of the control and management of power systems. Network planners strive to achieve an optimum level of investments and reliability. Network operators on the other hand aim at mitigating the costs associated with low levels of reliability. Effective decision making requires the management of uncertainties in the process applied. Thus, the modelling of reliability inputs, methodology applied in assessing network reliability and the interpretation of the reliability outputs should be carefully considered in reliability analyses. This thesis applies probability density functions, as opposed to deterministic averages, to model component failures. The probabilistic models are derived from historical failure data that is usually confined to finite ranges. Thus, the Beta distribution which has the unique characteristic of being able to be rescaled to a different finite range is selected. The thesis presents a new reliability evaluation technique that is based on the sequential Monte Carlo simulation. The technique applies a time-dependent probabilistic modelling approach to network reliability parameters. The approach uses the Beta probability density functions to model stochastic network parameters while taking into account seasonal and time-of- day influences. While the modelling approach can be applied to different aspects such as intermittent power supply and system loading, it is applied in this thesis to model the failure and repair rates of network components. Unlike the conventional sequential Monte Carlo methods, the new technique does not require the derivation of an inverse translation function for the probability distribution applied. The conventional Monte Carlo technique simulates the up and down component states when building their chronological cycles. The new technique applied here focuses instead on simulating the down states of component chronological cycles. The simulation determines the number of down states, when they will occur and how long they will last before developing the chronological cycle. Tests performed on a published network show that focussing on the down states significantly improves the computation times of a sequential Monte Carlo simulation. Also, the reliability results of the new sequential Monte Carlo technique are more dependent on the input failure models than on the number of simulation runs or the stopping criterion applied to a simulation and in this respect gives results different from present standard approaches. The thesis also applies the new approach on a real bulk power network. The bulk network is part of the South African power grid. Thus, the network threats considered and the corresponding failure data collected are typical of the real South African conditions. The thesis shows that probability density functions are superior to deterministic average values when modelling reliability parameters. Probability density functions reflect the variability in reliability parameters through their dispersion and skewness. The time-dependent probabilistic approach is applied in both planning and operational reliability analyses. The component failure models developed show that variability in network parameters is different for planning and operational reliability analyses. The thesis shows how the modelling approach is used to translate long-term failure models into operational (short-term) failure models. DigSilent and MATLAB software packages are used to perform network stability and reliability simulations in this thesis. The reliability simulation results of the time-dependent probabilistic approach show that the perception on a network's reliability is significantly impacted on when probability distribution functions that account for the full range of parameter values are applied as inputs. The results also show that the application of the probabilistic models to network components must be considered in the context of either network planning or operation. Furthermore, the risk-based approach applied to the interpretation of reliability indices significantly influences the perception on the network's reliability performance. The risk-based approach allows the uncertainty allowed in a network planning or operation decision to be quantified

A Parallel Fast-Track Service Restoration Strategy Relying on Sectionalized Interdependent Power-Gas Distribution Systems

In the distribution networks, catastrophic events especially those caused by natural disasters can result in extensive damage that ordinarily needs a wide range of components to be repaired for keeping the lights on. Since the recovery of system is not technically feasible before making compulsory repairs, the predictive scheduling of available repair crews and black start resources not only minimizes the customer downtime but also speeds up the restoration process. To do so, this paper proposes a novel three-stage buildup restoration planning strategy to combine and coordinate repair crew dispatch problem for the interdependent power and natural gas systems with the primary objective of resiliency enhancement. In the proposed model, the system is sectionalized into autonomous subsystems (i.e., microgrid) with multiple energy resources, and then concurrently restored in parallel considering cold load pick-up conditions. Besides, topology refurbishment and intentional microgrid islanding along with energy storages are applied as remedial actions to further improve the resilience of interdependent systems while unpredicted uncertainties are addressed through stochastic/IGDT method. The theoretical and practical implications of the proposed framework push the research frontier of distribution restoration schemes, while its flexibility and generality support application to various extreme weather incidents.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Quantification and mitigation of the impacts of extreme weather on power system resilience and reliability

Modelling the impact of extreme weather on power systems is a computationally expensive, challenging area of study due to the diversity of threats, complicatedness of modelling, and data and simulation requirements to perform the relevant studies. The impacts of extreme weather – specifically wind – are considered. Factors such as the distribution of outage probability on lines and the potential correlation with wind power generation during storms are investigated; so too is sensitivity of security assessments involving extreme wind to the relationships used between failures and the natural hazard being studied, specifically wind speed. A large scale simulation ensemble is developed and demonstrated to investigate what are deemed the most significant features of power system simulation during extreme weather events. The challenges associated with modelling high impact low probability (HILP) events are studied and demonstrate that the results of security assessments are significantly affected by the granularity of incident weather data being used and the corrections or interpolation being applied to the source data. A generalizable simulation framework is formulated and deployed to investigate the significance of the relationship between incident natural hazards, in this case wind, and its corresponding impact on system resilience. Based on this, a large-scale simulation model is developed and demonstrated to take consideration of a wide variety of factors which can affect power systems during extreme weather events including, but not limited to, under frequency load shedding, line overloads, and high wind speed shutdown and its impact on wind generation. A methodology for quantifying and visualising distributed overhead line failure risk is also demonstrated in tandem with straightforward methods for making wind power projections over transmission systems for security studies. The potential correlation between overhead line risk and wind power generation risk is illustrated visually on representations of GB power networks based on real world data.Open Acces

Integration of Preventive and Emergency Responses to Boost Distribution System Resilience

Recent years have seen a series of large-scale blackouts due to extreme weather events around the world. These high impact, lower probability events have caused great economic losses to modern society. Therefore, it is urgent to study the resilience improvement measures of power systems to mitigate the effects of adverse extreme events. Current research mainly focuses on the hardening measures where robust optimization is used to solve the problems. However, due to the consideration of worst case of uncertain parameters, the robust optimization method is usually too conservative and uneconomical in many situations. In this thesis, operational measures are deployed to boost the distribution system resilience considering all possible scenarios. An integrated resilience response framework is proposed, which provides distribution system operators solutions to address the resilience enhancement problem in both preventive state and emergency states. The key of the framework is a two-stage stochastic mix-integer linear optimization model. The mathematical formulation and the solving method, progressive hedging algorithm, are presented in this thesis as well. Preventive response includes topology reconfiguration and generator redispatch, while topology reconfiguration, generator redispatch and load curtailment are allowed in emergency response. Case study on IEEE 33 bus system and a modified 69 bus system validates the correctness and effectiveness of the proposed framework and model. Integrated response solution is obtained by solving the model and sensitivity analysis is performed to study the performance of integrated response under different system parameters. The key conclusions include the following: 1) integrated response improve distribution system resilience in a minimum cost; 2) integrated response is preferable to either individual preventive or emergency response; 3) system parameters and abilities such as unit load shedding cost, ramping ability and generator availability influence the system resilience and expected total cost in different degrees

Integration of Preventive and Emergency Responses to Boost Distribution System Resilience

Recent years have seen a series of large-scale blackouts due to extreme weather events around the world. These high impact, lower probability events have caused great economic losses to modern society. Therefore, it is urgent to study the resilience improvement measures of power systems to mitigate the effects of adverse extreme events. Current research mainly focuses on the hardening measures where robust optimization is used to solve the problems. However, due to the consideration of worst case of uncertain parameters, the robust optimization method is usually too conservative and uneconomical in many situations. In this thesis, operational measures are deployed to boost the distribution system resilience considering all possible scenarios. An integrated resilience response framework is proposed, which provides distribution system operators solutions to address the resilience enhancement problem in both preventive state and emergency states. The key of the framework is a two-stage stochastic mix-integer linear optimization model. The mathematical formulation and the solving method, progressive hedging algorithm, are presented in this thesis as well. Preventive response includes topology reconfiguration and generator redispatch, while topology reconfiguration, generator redispatch and load curtailment are allowed in emergency response. Case study on IEEE 33 bus system and a modified 69 bus system validates the correctness and effectiveness of the proposed framework and model. Integrated response solution is obtained by solving the model and sensitivity analysis is performed to study the performance of integrated response under different system parameters. The key conclusions include the following: 1) integrated response improve distribution system resilience in a minimum cost; 2) integrated response is preferable to either individual preventive or emergency response; 3) system parameters and abilities such as unit load shedding cost, ramping ability and generator availability influence the system resilience and expected total cost in different degrees

Value of thermostatic loads in future low-carbon Great Britain system

This paper quantifies the value of a large population of heterogeneous thermostatically controlled loads (TCLs). The TCL dynamics are regulated by means of an advanced demand side response model (DSRM). It optimally determines the flexible energy/power consumption and simultaneously allocates multiple ancillary services. This model explicitly incorporates the control of dynamics of the TCL recovery pattern after the provision of the selected services. The proposed framework is integrated in a mixed integer linear programming formulation for a multi-stage stochastic unit commitment. The scheduling routine considers inertia-dependent frequency response requirements to deal with the drastic reduction of system inertia under future low-carbon scenarios. Case studies focus on the system operation cost and CO2 emissions reductions for individual TCLs for a) different future network scenarios, b) different frequency requirements, c) changes of TCL parameters (e.g. coefficient of performance, thermal insulation etc.)

Analytical Approach for Active Distribution Network Restoration Including Optimal Voltage Regulation

The ever increasing utilization of sensitive loads in the industrial, commercial and residential areas in distribution networks requires enhanced reliability and quality of supply. This can be achieved thanks to self healing features of smart grids that already include the control technologies necessary for the restoration strategy in case of a fault. In this paper, an analytical and global optimization model is proposed for the restoration problem. A novel mathematical formulation is presented for the reconfiguration problem reducing the number of required binary variables while covering more practical scenarios compared to the existing models. The considered self healing actions besides the network reconfiguration are the nodal load rejection, the tap setting modification of voltage regulation devices (incl. OLTCs, SVR, and CBs), and the active or reactive power dispatch of DGs. The voltage dependency of loads is also considered. Thus, the proposed optimization problem determines the most efficient restoration plan minimizing the number of deenergized nodes with the minimum number of self healing actions. The problem is formulated as a Mixed Integer Second Order Cone Programming (MISOCP) and solved using the Gurobi solver via the MATLAB interface YALMIP. A real 83 node distribution network is used to test and verify the presented methodology