Robert John Millar

Impact of MV Connected Microgrids on MV Distribution Planning

The planning and development of distribution networks with a substantial penetration of microgrids connected to the medium voltage (MV) network form the main themes of this paper. The impact of microgrids is assessed in terms of their effect on optimal network topology, losses, reliability, reserve connections, network upgrade and expansion savings. The earning base of the distribution system operator also comes under scrutiny. A suburban MV cable network is planned using a network planning algorithm developed by the authors, first with optimal routing for demand-only nodes and then with a 33% penetration of randomly located microgrids. The network is then expanded to meet the requirements of a future planning horizon, in order to compare the expanded and upgraded optimum MV network topology with and without microgrids. Apart from visually depicting the topological differences, the savings such microgrids can give to the long term distribution network investment and running costs are quantified in terms of the investment costs, loss costs and interruption costs. When networks are planned with optimal rather than full backup, the introduction of microgrids is shown to have a considerable saving impact on all cost components except the cost per unit power transfer in the distribution network.

Comparison of Air-Gap Thermal Models for MV Power Cables Inside Unfilled Conduit

This paper studies the effects of natural convection on longitudinal heat transfer and on the air-gap thermal resistance of cables inside conduit installations. Oversimplification of the physical placement of cables inside unfilled conduits is the main shortcoming in currently available thermal models. The study closely investigates the share of each heat-transfer mechanism and the effect of the natural placement of trefoil cables inside the conduit. Measurements from various installation setups are investigated for their impact on heat transfer. The installation-dependent convection correlations adopted in this study have broader applications for the dynamic thermal rating of underground cables inside conduit, troughs, and tunnels. Laboratory measurements are compared with numerical solutions from the IEC 60287 standards, Electra 143 methods, and FEA simulations.

Dynamic Thermal Modeling of MV/LV Prefabricated Substations

With the expansion and infilling of urban areas, the demand for electric power is driving the design and capacity of distribution substations to their thermal limits. Distribution transformer substations are increasingly required to be compact, reliable, safe, and intelligent. To efficiently utilize city space and to support the intermittent load flows imposed by smart-grid features, such as distributed generation, the transformers are expected to operate close to or occasionally over their ratings, with stalled or little air circulation inside the safety enclosure. Dynamic thermal models with physically validated convection and radiation heat-transfer components are essential for the real-time thermal rating of substations. Natural convection via the air inside the cabin to the outside ambient air plays the major role in cooling down a transformer. In this study a scale model of a prefabricated substation is examined to draft a numerical solution which is based on stack ventilation principles. A clear and expandable first principle approach is used to quantify heat transfer through ventilation openings. Measurements from actual cabins and 3-D finite element method simulations are used to validate the numerical model.

A Statistical Model for Hourly Large-Scale Wind and Photovoltaic Generation in New Locations

The analysis of large-scale wind and photovoltaic (PV) energy generation is of vital importance in power systems, where their penetration is high. This paper presents a modular methodology to assess the power generation and volatility of a system consisting of both PV plants (PVPs) and wind power plants (WPPs) in new locations. The methodology is based on statistical modeling of PV and WPP locations with a vector autoregressive model, which takes into account both the temporal correlations in individual plants and the spatial correlations between the plants. The spatial correlations are linked through distances between the locations, which allow the methodology to be used to assess scenarios with PVPs and WPPs in multiple locations without actual measurement data. The methodology can be applied by the transmission and distribution system operators when analyzing the effects and feasibility of new PVPs and WPPs in system planning. The model is verified against hourly measured wind speed and solar irradiance data from Finland. A case study assessing the impact of the geographical distribution of the PVPs and WPPs on aggregate power generation and its variability is presented.

Criticality Analysis of Failure to Communicate in Automated Fault-Management Schemes

The effectiveness of various functions in a smart distribution grid relies on a reliable communication system. These functions are normally developed based on specific automation schemes. The majority of automated fault-management schemes (AFMSs) requires a dependable communication system to accomplish their intended functions. In a smart distribution grid, the AFMSs are responsible for automatic fault detection, location, isolation, and service restoration activities. Failure to communicate among various communicating devices involved in the AFMS may have a considerable impact on the reliability of electricity service delivered to the customers. This paper aims to quantitatively evaluate the reliability impact of failure to communicate in the AFMS. A highly loaded urban distribution network equipped with AFMS is utilized for directing the quantitative reliability case studies. The study results show that the service reliability can be significantly affected if the communication system fails to operate successfully.

Dynamic thermal state forecasting of distribution network components: For enhanced active distribution network capacity

This paper proposes and investigates a framework for day-ahead hour-by-hour thermal state forecasting of distribution network components. The method takes into consideration the uncertainty brought about by distributed generation and load demand forecasting. Potential risks are evaluated so that contingency measures can be planned with a clear knowledge of tolerance to temporary overloading scenarios.

A Framework to Split the Benefits of DR Between Wind Integration and Network Management

In power systems today, considerable developments are being made in the energy transition from centralized fossil fuels to renewable distributed generation (DG) sources. However, this ongoing progress has presented several challenges to the operation and planning of distribution systems due to the variability of intermittent renewable generation. Demand response (DR) is widely regarded as a feasible tool to provide a seamless transition by altering the load profiles according to the intermittent generation profile. However, the resultant volatile power flows can be taxing for network capacity. This paper offers a framework from the distribution system operators perspective for the optimal utilization of DR between DG curtailment mitigation and network management. The application of the developed framework to a generic Finnish distribution system demonstrates that the benefits of DR should be envisioned for network management as long as the wind curtailment rate is below a certain level. This means that, beyond the threshold energy curtailment rate, the distribution system operator would be more economically efficient by making network reinforcements.

Probabilistic prosumer node modeling for estimating planning parameters in distribution networks with renewable energy sources

With the increase in distributed generation, the demand-only nature of many secondary substation nodes in medium voltage networks is becoming a mix of temporally varying consumption and generation with significant stochastic components. Traditional planning, however, has often assumed that the maximum demands of all connected substations are fully coincident, and in cases where there is local generation, the conditions of maximum consumption and minimum generation, and maximum generation and minimum consumption are checked, again assuming unity coincidence. Statistical modelling is used in this paper to produce network solutions that optimize investment, running and interruption costs, assessed from a societal perspective. The decoupled utilization of expected consumption profiles and stochastic generation models enables a more detailed estimation of the driving parameters using the Monte Carlo simulation method. A planning algorithm that optimally places backup connections and three layers of switching has, for real-scale distribution networks, to make millions of iterations within iterations to form a solution, and therefore cannot computationally afford millions of parallel load flows in each iteration. The interface that decouples the full statistical modelling of the combinatorial challenge of prosumer nodes with such a planning algorithm is the main offering of this paper.