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The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency’s nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel constraint from the swing equation to contain the frequency nadir using all of these services. The expected cost of UFLS is found probabilistically to facilitate its comparison to the other frequency services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone (SOC), resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to $\pounds $ 559 m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored. [ABSTRACT FROM AUTHOR]

The foreseen development of large-scale Offshore Windfarms (OWFs) further from the shore dictates that the OWF transmission system must be optimally designed based on Dynamic Thermal Rating (DTR) in order to fully utilize the intermittent nature of the wind and to keep the offshore wind cost-competitive. In this paper, a comprehensive, DTR-based, two-stage stochastic model is presented, which has been developed for investment decision support for OWF size and HVAC transmission systems. Complex DTR models for all the critical HV components are made fit for the mixed-integer linear programming problem, while accounting for the stochasticity in wind generation and component availability. The main decisions incorporate the discrete size of OWFs, HV subsea export cable cross-sections and ratings for transformers and shunt reactors. For validation, an actual testcase OWF off the east coast of U.K. has been used. Results indicate that DTR-based iterative design of OWF and its transmission components can significantly improve the business case, even though transmission efficiency and energy delivered are not maximum for the optimal design [ABSTRACT FROM AUTHOR]

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region. [ABSTRACT FROM AUTHOR]

The undergoing transition from conventional to converter-interfaced renewable generation leads to significant challenges in maintaining frequency stability due to declining system inertia. In this paper, a novel control framework for Synthetic Inertia (SI) provision from Wind Turbines (WTs) is proposed, which eliminates the secondary frequency dip and allows the dynamics of SI from WTs to be analytically integrated into the system frequency dynamics. Furthermore, analytical system frequency constraints with SI provision from WTs are developed and incorporated into a stochastic system scheduling model, which enables the provision of SI from WTs to be dynamically optimized on a system level. Several case studies are carried out on a Great Britain 2030 power system with different penetration levels of wind generation and inclusion of frequency response requirements in order to assess the performance of the proposed model and analyze the influence of the improved SI control scheme on the potential secondary frequency dip. The results demonstrate that the inclusion of SI provision from WTs into Unit Commitment (UC) can drastically impact the overall system costs. [ABSTRACT FROM AUTHOR]

Phase imbalance widely exists in the UK's low voltage (415 V, LV) distribution networks. The imbalances not only lead to insufficient use of LV network assets but also cause energy losses. They lead to hundreds of millions of British pounds each year in the UK. The cost-benefit analyses of phase balancing solutions remained an unresolved question for the majority of the LV networks. The main challenge is data-scarcity – these networks only have peak current and total energy consumption that are collected once a year. To perform a cost-benefit analysis of phase balancing for data-scarce LV networks, this paper develops a customized cluster-wise Gaussian process regression (CGPR) approach. The approach estimates the total cost of phase imbalance for any data-scarce LV network by extracting knowledge from a set of representative data-rich LV networks and extrapolating the knowledge to any data-scarce network. The imbalance-induced cost is then translated into the benefit from phase balancing and this is compared against the costs of phase balancing solutions, e.g., deploying phase balancers. The developed CGPR approach assists distribution network operators (DNOs) to evaluate the cost-benefit of phase balancing solutions for data-scarce networks without the need to invest in additional monitoring devices. [ABSTRACT FROM AUTHOR]

Using their fast power reduction capabilities, the induction motor variable speed drives can successfully participate in the power system primary frequency control. A new control strategy for fast frequency support from the diode front-end induction motor variable speed drive is presented in this paper. By this, practical restrictions are considered. In this context, an existing conventional scheme presented in literature is firstly modified by deploying an estimated motor's power losses for a more exactly control design. Next, a novel control scheme is proposed. It attains the fastest frequency support from the drive and simultaneously restricts its minimum power consumption to a given positive value to prevent regeneration mode during the frequency support process. The frequency of applied voltage to the motor is adjusted through a proportional-integrator compensator with deviation of the drive's consumption power from its reference power as input. The reference power is synthesized by permanent and temporary components, which are determined based on deviation and time derivative of the power system's frequency, respectively. The effectiveness of the proposed smart drive for the primary frequency control is investigated using the New England 39 bus and a more realistic Great Britain 36 zone test networks. It improves system frequency response in terms of the rate of change of frequency and the frequency nadir. Furthermore, the new control scheme can contribute to quickly damping inter-area oscillations in multi-machine power systems. [ABSTRACT FROM AUTHOR]

The paper discusses the modeling and the simulation of the limiters of proportional integral (PI) controllers included in voltage-sourced converters. Seven PI controller models are considered. These include windup and anti-windup limiters, back calculation with different gains and delayed feedback and the model recommended by the IEEE standard 421.5-2016. The dynamic behavior and numerical issues arising for each PI implementation are thoroughly compared by means of a detailed all-island dynamic model of the Irish system including the HVDC East–West Interconnector and a simplified dynamic model of the Great Britain system; and the Nordic system with inclusion of a STATCOM device. Recommendations on the computer-based implementation of each model are provided in the concluding remarks. [ABSTRACT FROM AUTHOR]

The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimizing the provision of these services in the scheduling process, subject to significant uncertainty, is a complex task given the challenge of linking the steady-state optimization with frequency dynamics. This paper proposes a novel frequency-constrained stochastic unit commitment model which, for the first time, co-optimizes energy production along with the provision of synchronized and synthetic inertia, enhanced frequency response, primary frequency response and a dynamically-reduced largest power infeed. The contribution of load damping is modeled through a linear inner approximation. The effectiveness of the proposed model is demonstrated through several case studies for Great Britain's 2030 power system, which highlight the synergies and conflicts among alternative frequency services, as well as the significant economic savings and carbon reduction achieved by simultaneously optimizing all these services. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) has been traditionally applied adopting moderate settings at primary substations and when distributed generation was uncommon. However, as new infrastructure is deployed across European-style medium voltage (MV) and low voltage (LV) networks, driven by increasing photovoltaic (PV) penetration levels, the opportunity arises to develop more advanced CVR schemes. This paper proposes a centralized, three-phase AC optimal power flow (OPF)-based CVR scheme that, using monitoring, on-load tap changers and capacitors across MV and LV, actively manages voltages to minimize energy consumption, even with high PV penetration, while considering MV–LV constraints. To tackle scalability issues brought by discrete variables, a two-stage approach is proposed to solve the OPF as a non-linear programming problem (relaxing integer variables). A process that continuously checks customer voltages is adopted to trigger the optimization only when needed. Moreover, CVR benefits are not only quantified at a network level but also for customers, providing useful insights to policy makers. The proposed control is assessed using a realistic, unbalanced U.K. residential MV–LV network (2,400+ customers) with high PV penetration, and 1-min resolution time-varying profiles and load models. Results demonstrate that the proposed control effectively coordinates voltage regulation in MV and LV levels throughout the day, minimizing energy imports for all customers. [ABSTRACT FROM AUTHOR]

Phase imbalance in the U.K. and European low-voltage (415 V, LV) distribution networks causes additional energy losses. A key barrier against understanding the imbalance-induced energy losses is the absence of high-resolution time-series data for LV networks. It remains a challenge to estimate imbalance-induced energy losses in LV networks that only have the yearly average currents of the three phases. To address this insufficient data challenge, this paper proposes a new customized statistical approach, named as the clustering, classification, and range estimation (CCRE) approach. It finds a match between the network with only the yearly average phase currents (the data-scarce network) and a cluster of networks with time series of phase current data (data-rich networks). Then, CCRE performs a range estimation of the imbalance-induced energy loss for the cluster of data-rich networks that resemble the data-scarce network. Chebyshev's inequality is applied to narrow down this range, which represents the confidence interval of the imbalance-induced energy loss for the data-scarce network. Case studies reveal that, given such a few data from the data-scarce networks, more than 80% of these networks are classified to the correct clusters and the confidence of the imbalance-induced energy loss estimation is 89%. [ABSTRACT FROM AUTHOR]

In the Great Britain power system, reduced system inertia (particularly during low demand conditions) and larger possible infeed loss would make grid frequency regulation extremely challenging in future. Traditional primary frequency response could be insufficient to limit the frequency variation within acceptable range. This paper shows that thermostatically controlled loads (TCLs) (domestic refrigerators) can be controlled without real-time communication and in a nondisruptive way to collectively enhance the network frequency response. The aggregated power consumption of TCLs, distributed across the system, could be controlled as a ‘linear’ function of the locally measured frequency and its rate of change. Alternatively, their aggregated consumption could be made to follow a ‘pre-set’ power profile depending on the estimated infeed loss. A novel technique for accurate estimation of infeed loss and consequent postfault TCL power reduction is also proposed. The effectiveness of the two TCL control strategies is compared for primary and secondary frequency response through a case study on a 36 busbar reduced equivalent of the Great Britain power system. The effect of spatial variation of transient frequencies and the time delays in frequency measurement and filtering are considered to show how the TCLs can realistically provide rapid frequency response. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-resolution clustering (MRC) method that for the first time classifies end customers directly from massive, volatile and uncertain smart metering data. It will firstly extract spectral features of load profiles by multi-resolution analysis (MRA), and then cluster and classify these features in the spectral-domain instead of time-domain. The key advantage is that the proposed method will allow a dynamic load profiling to be flexibly re-constructed from each spectral level. MRC addresses three key limitations in time-series based load profiling: i) large sample size: sample size is reduced by a novel two-stage clustering, which firstly clusters each customers' massive daily profiles into several Gaussian mixture models (GMMs) and then clusters all GMMs; ii) volatility: it avoids the interferences between different load features (e.g. magnitude, overall trend, spikes) by decomposing them onto different resolution levels, and then clustering separately; iii) uncertainties: as the GMM can give a probabilistic cluster membership instead of a deterministic one, an additive classification model based on the posterior probability is proposed to reflect the uncertainty between days. The proposed method is implemented on 6369 smart metered customers from Ireland, and compared with the load profiles used by the U.K. industry and traditional K-means clustering. The results show that the developed MRC outperformed the traditional methods in its ability in profiling load for big, volatile and uncertain smart metering data. [ABSTRACT FROM PUBLISHER]

This paper presents a framework for assessing the economic efficiency of different long-run network pricing models. The aim is to provide a quantifiable efficiency measure that will inform regulators, network operators, and network users when an alternative pricing model is contemplated. The efficiency measure is derived from the long-term network development costs that flow from a dynamic interaction between network pricing and planning; that is the response of network users to differing charging models, the consequential network investment, and new financial incentives that follow reinforcement of the network. To illustrate the approach, the framework has been used to assess the relative efficiency of three broad class of distribution pricing models in a study undertaken for the Great Britain (GB)'s regulator for gas and electricity markets in England, Wales, and Scotland-Ofgem. The study contemplated three pricing models that are used in practice. The efficiency assessment in the Ofgem study was conducted on a pseudo distribution reference network. On the assumption that the results from the reference network could be scaled up to the national level, then the assessment suggests that by moving from the present DRM model to the more economic LRIC approach, GB distribution network operators could save in the region of £200 m over the next 20 years in their network investment costs. This paper draws heavily on the project undertaken for Ofgem, but any views expressed are solely those of the authors. [ABSTRACT FROM AUTHOR]

This two-part paper surveys the frequency and voltage control ancillary services in power systems from various parts of the world. In this second part, essential economic features that must be taken into account when designing markets for ancillary services are first discussed. The methods adopted in eight different systems (Australia, France, Germany, Great Britain, New Zealand, PJM, Spain, and Sweden) for trading frequency and voltage control ancillary services are then compared. Lastly, new indicators for comparing volumes and costs of ancillary services across systems are proposed and calculated for the systems included in this survey. The companion paper surveys the technical features of these ancillary services. [ABSTRACT FROM AUTHOR]

This paper reports on the use of multi-agent system technology to automate the management and analysis of SCADA and digital fault recorder (DFR) data. The multi-agent system, entitled Protection Engineering Diagnostic Agents (PEDA), integrates legacy intelligent systems that analyze SCADA and DFR data to provide data management and online diagnostic information to protection engineers. Since November 2004, PEDA agents have been intelligently interpreting and managing data online at a transmission system operator in the U.K. As the results presented in this paper demonstrate, PEDA supports protection engineers by providing access to interpreted power systems data via the corporate intranet within minutes of the data being received. In this paper, the authors discuss their experience of developing a multi-agent system that is robust enough for continual online use within the power industry. The use of existing agent development toolsets and standards is also discussed. [ABSTRACT FROM AUTHOR]

Electricity demand forecasts are needed for decisions regarding generation dispatch for lead times as short as just a few minutes. Imbalance between generation and demand causes deviation of the system frequency from its target, which in Great Britain is 50 Hz. This, in turn, causes a change in demand, due largely to motor loads. For Great Britain, the change is estimated to be 2.5% of demand per 1 Hz of frequency deviation from its target. This can be used to calculate the demand that would have occurred if frequency had been at 50 Hz. Modeling and forecasting the resulting frequency-corrected demand provides a better basis for dispatching generation. This paper evaluates methods for forecasting frequency-corrected demand up to 10 min ahead. We introduce an exponential smoothing model that, like the system operator's proposed Kalman filter approach, jointly models frequency and demand. We also evaluate a set of univariate methods applied directly to the series of frequency-corrected demand. These methods have not previously been considered for lead times less than 10 min. In our empirical analysis, the best results were produced by a seasonal exponential smoothing method applied directly to the series of frequency-corrected demand. [ABSTRACT FROM AUTHOR]

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts. [ABSTRACT FROM PUBLISHER]

This paper presents a method to quantify and rank transmission system components by their importance for system reliability under different load scenarios. Each component is ranked by three separate importance indexes based on its expected outage rate and impact on: 1) system security margin; 2) load supply; and 3) generation units. By studying these three interests individually, a more complete view of the risks to system reliability can be assessed. The method is demonstrated on a detailed power system model (7000 components) of a significant part of the Great Britain transmission system at 400 and 275 kV. The results show how sensitive the component indexes are to the load scenario. The method provides an input for decision-making when planning maintenance and new investment and can be used as a complement to deterministic criteria. [ABSTRACT FROM PUBLISHER]

This paper deals with the identification of network configuration changes in distribution system state estimation (DSSE). The models of critical network configurations are stored in the form of a model bank. A recursive Bayesian approach which utilizes the output of the state estimation function of each model in the bank is used to identify the correct configuration of the network. The concept is demonstrated on a part of the U.K. Generic Distribution System (UKGDS) model. [ABSTRACT FROM AUTHOR]

Optimal reactive power flow applications often model large numbers of discrete shunt devices as continuous variables, which are rounded to their nearest discrete value at the final iteration. This can degrade optimality. This paper presents novel methods based on probabilistic and adaptive threshold approaches that can extend existing security constrained optimal reactive power flow methods to effectively solve large-scale network problems involving discrete shunt devices. Loss reduction solutions from the proposed techniques were compared to solutions from the mixed integer nonlinear mathematical programming algorithm (MINLP) using modified IEEE standard networks up to 118 buses. The proposed techniques were also applied to practical large-scale network models of Great Britain. The results show that the proposed techniques can achieve improved loss minimization solutions when compared to the standard rounding method. [ABSTRACT FROM AUTHOR]

Location of the static VAR compensator (SVC) and other types of shunt compensation devices is important for the enhancement of the voltage stability for practical power systems. With the theory of the normal forms of diffeomorphism, this paper proposes a new method to solve this problem. The proposed method makes use of the nonlinear participation factors, in which the nonlinearity of power systems can be taken into consideration. As a result, the most suitable location where the SVC should be used in power system can be determined, even for the cases in which the system is characterized with strong nonlinearity. In order to show the effectiveness of the proposed method, the New England 39-bus power system with SVC is used as an example. Calculation results show that with the SVC located at the place where the proposed method determined, the voltage stability is considerably enhanced. The steady-state voltage stability index and the time domain simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

If transmission charges are to reflect costs, they should be affected by the location of demand and generation. This paper describes the investment cost-related pricing (ICRP) methodology used to calculate transmission charges in Great Britain (GB), which is based on the marginal investment cost of additional demand or generation, using a dc load flow transport model. We apply this existing method to calculate charges for the Supergen FutureNet scenarios for 2020. This study highlights the sensitivities in charges for use of the transmission system arising from plausible demand and generation developments. The changes in tariffs will present financial challenges for system users in some areas. The objective of the work presented is to illustrate the sensitivity of the charges produced by this methodology to changes in demand, generation, and network topology rather than compare alternative pricing approaches. The conclusion drawn is that the ICRP system pricing method may be suitable in future years but only with some important issues investigated and resolved. [ABSTRACT FROM AUTHOR]

Regulators are aiming to incentivize developers and Distribution Network Operators to connect distributed generation (DG) to improve network environmental performance and efficiency. A key question is whether these incentives will encourage both parties to connect DG. Here, multiobjective optimal power flow is used to simulate how the parties' incentives affect their choice of DG capacity within the limits of the existing network. Using current U.K. incentives as a basis, this paper explores the costs,, benefits and tradeoffs associated with DG in terms of connection, losses and, in a simple fashion, network deferral. [ABSTRACT FROM AUTHOR]

This paper discusses an alternative to the ex post pricing model currently under development within ISO New England's (ISO NE) ancillary service market. Since ISO NE's real-time ancillary service market co-optimizes both energy and reserve products, ex post pricing is needed for both energy and reserve. A co-optimization approach for ex post pricing requires defining complex pricing rules and implementing heuristics to ensure consistent binding constraints with the ex ante dispatch. In order to avoid complexity and uncertainty of required heuristics, a new ex post pricing schema is proposed to calculate the ex post energy and reserve prices in a decoupled way while recognizing the coupling effect of energy and reserve. In the proposed approach, ex post energy prices are first computed by incorporating the ex ante marginal opportunity cost of reserve in the energy offer; the ex post reserve prices are then calculated by considering the ex post marginal opportunity cost of energy. This approach simplifies the implementation of ex post pricing rules in the ISO NE market. Numerical examples are presented to further demonstrate the validity of this approach. [ABSTRACT FROM AUTHOR]

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3°C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months. [ABSTRACT FROM AUTHOR]