Toward a Retail Market for Distribution Grids

Ancillary services e.g., voltage control, congestion management and frequency control, require to be compensated increasingly from the Distributed Energy Resources (DERs). DERs are predominantly wind and photo-voltaic power plants, the major share of which are installed at the distribution grid level. Therefore, the previously passive distribution grids require transformation towards a more active role. Provision of ancillary services from the distribution grid level, requires assessment of active and reactive power flexibility (PQ-flexibility) potentials. Furthermore, increased renewable penetration correlates to increased responsibility of the Distribution System Operators (DSOs) for assessing the flexibility potentials. An aggregation of distribution grid potentials, subject to technical grid constraints and technological power limitations, is termed as Feasible Operating Region (FOR) of the distribution grid. The FOR effectively serves as an interface between the DSOs and the Transmission System Operators (TSO), for flexibility exchanges and planning of ancillary services provision. The determination of the FOR is established in current research, using different algorithms e.g stochastic methods, meta-heuristic programming and mathematical optimization techniques. In this paper, an FOR determination algorithm using successive linear programming (sLP) is proposed and validated against established optimization approaches on a uniform medium voltage (MV) grid model. Comparisons reveal competitiveness with established methods and an added advantage of fast calculation times, suitable for real time assessments. Further enhancement of the FOR is proposed, by integrating discrete transformer tap-changing operational flexibilities using a successive mixed integer linear programming (sMILP). Results demonstrate an increase in the flexibility potential from the distribution grid.

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs).In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation(SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

Integrating distributed energy resources in electricity distribution systems: An explorative study of challenges facing DSOs in Sweden

During a blackout, resupplying critical infrastructure through microgrids at the distribution grid level can significantly mitigate societal damage. This requires automated planning of switching operations to establish a stable microgrid structure while ensuring that operational limits are maintained. This study presents a software-based approach for automated microgrid planning for a blackout scenario. The developed software analyzes grid data, determines feasible switching operations, and performs load flow calculations to ensure a stable supply. Validation was conducted using the IEEE 9-Bus system and a real distribution grid. Special attention was paid to reactive power management, which proved to be a critical challenge during microgrid formation.

The present paper deals with the topic of the integration of Energy Storage Systems (ESSs) in Medium Voltage (MV) distribution grids, managed by Distribution System Operators (DSOs), in case of significant presence of Dispersed Generation (DG), typically based on Renewable Energy Sources (RESs). A real case of installation of a Li-Ion Batteries ESS, by the Italian DSO Enel Distribuzione, in a High Voltage (HV) / Medium Voltage (MV) station in the Italian power system, has been object of study. For this purpose, a MatLab software, made up of more tools, has been implemented. It allows interfacing a proprietary database of Enel Distribuzione to MatPower tool and performing multi-period Power Flow (PF) analyses. The software has been used to evaluate the performance of the real ESS in the real MV grid, for active power peak shaving and reactive power compensation functions, in respect to the HV grid. With a critical analysis of the results, the effectiveness of the installation of the ESS on the real network has been tested.

The widespread use of distributed energy resources (DERs) in the distribution grid is changing the electricity sector worldwide in many ways. The integration of distributed renewable energy (DRE), demand response, and smart grid technologies (SGTs) can make the power systems more sustainable, efficient, and reliable. However, new entities and market mechanisms are needed to efficiently integrate those resources in the distribution grid. This paper presents a day-ahead energy and reserve market clearing and settlement model for a distribution system operator (DSO) considering the uncertainties of DRE at different nodes of the distribution grid. The problem is formulated as a stochastic optimization model, whose objective is to minimize the expected system operation cost over a specified time horizon. The problem is then solved using a Monte Carlo simulation algorithm based on sample average approximation (SAA). A case study is performed to demonstrate the effectiveness of the proposed model.

A notable spread of Dispersed Generation (DG) power plants, connected at Medium Voltage (MV) distribution grids, takes place in the Italian power system, with a big impact on the operation, both in normal and in emergency conditions, especially during transients determining frequency variations. Enel Distribuzione, the biggest Distribution System Operator (DSO) in Italy, proposed to install some special devices, on its distribution grid, to improve the effectiveness of management of DG and loads, during frequency transients in automatic way. The paper describes the solution proposed by Enel Distribuzione, that gives the possibility to better manage the DG and loads connected in MV in favor of the whole power system in presence of frequency transients, as the Italian Transmission System Operator (TSO) with its Defense Plan (DP) requires. Some numerical results for demonstrating the need and effectiveness of the application of the proposed solution in the real operation of the system are reported.

Enabling mass integration of electric vehicles through distributed energy resource management systems

Integration of flow battery for resilience enhancement of advanced distribution grids

Huge amounts of data are coming from the electrical grid, through smart meters, smart inverters, and Supervisory Control and Data Acquisition (SCADA) protocols. This makes emerging power systems, and especially distribution grids, heavily dependent on real-time data from the field, as well as on the IT infrastructure for observation and control of field devices. As a consequence, emerging electrical systems are becoming fully digitized, cyber-physical systems with all the accompanying advantages and challenges. Such a power system is called a “Smart Grid”. As all cyber-physical systems, Smart Grids are heavily dependent on Information and Communication Technology (ICT) infrastructure. Therefore, they are highly vulnerable to attacks that can compromise uninterrupted data flow, such as denial of service attacks and interruptions due to natural disasters, cataclysmic events, or wars, as well as due to malicious cyber-attacks. In this paper, we summarize the state-of-the-art in the cyberphysical security of distributed energy resources (DERs). We explore pain points to which monitoring and control of DERs in emerging electrical systems are exposed due to malicious cyberattacks. We continue with analyzing the accompanying consequences to the safety and reliability of emerging Smart Grids. Finally, we present our idea of Digital Twin technology as a tool for assisting distribution system operators and grid engineers, to detect, attenuate, and prevent malicious cyberattacks in DERs.

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

Cost assessment of storage options in a region with a high share of network congestions

Grid topology information plays an important role in grid observability applications such as fault detection and diagnosis. For these applications, data from customer connections should be processed jointly with measurements from the distribution grid by Distribution System Operator (DSO) systems and also correlated to the LV grid topology. In practical DSO systems, the LV grid topology data is frequently included in their databases and may come from different systems such as Geographical Information System (GIS) or other asset management systems, which store a relevant part of the grid topology in a type-specific format. However, in most cases, the grid topology information is not utilized for grid observability applications due to several challenges such as lack of standard data models, complexities in extracting topology information, incorrect/incomplete topology information, dependence on multiple databases etc. Thus, this paper presents challenges and complexities faced by electrical utilities in extracting/using grid topology information for observability applications. The challenges are demonstrated using topology models from two real medium-sized distribution grid operators, which are currently being used in two different European countries.

Distributed energy resources (DERs) are mostly low-power-generating units. In order to augment the energy demands, integration of such energy sources with LV distribution grid is a viable option. A good-quality power from DER ensures safe operation of distribution grid. In this work, an integrated DER consisting of a photovoltaic system, a wind energy conversion system and a micro-hydro system is proposed and coupled to a common DC bus through appropriate power electronic interfacing circuits. A grid interfacing inverter is implemented as an inter-tie between DC-coupled DER and LV distribution grid. The DER supports the DC link voltage of the interfacing inverter. The performance of the distribution grid is examined via connection of a 3-phase nonlinear load onto the distribution grid. The interfacing inverter incorporates the features of DSTATCOM. The effectiveness of DSTATCOM-DER topology in mitigating the power quality problems of nonlinear load is realized through Icosφ control strategy. The results obtained are adequately explained. The quality of power at the point of common coupling is analyzed using total harmonic distortion and total demand distortion and is found to be within IEEE Std. 519-2014. The complete system is modeled and simulated in MATLAB/Simulink.

A distribution energy resource aggregator (DERA) constitutes a group of distribution energy resources with small generation capacities that, when combined, meet the threshold to participate in the electricity wholesale market.This document investigates the DERA offer model formulation with three components of solar, battery energy storage system (BESS), and the demand response resources (DRRs).Different economical assessment methodologies have been developed to incorporate bids for individual distributed resources, including a solar offer model, BESS opportunity cost offer algorithm, and nested utility function model for DRRs.In addition, an optimization-based method and a cost-based method are proposed to aggregate individual cost offers to a DERA cost curve to bid in the wholesale electricity market while the schedule-following method and profit-following method are also introduced to simulate the DER actual dispatch.Based on the DERA models in this document, the SCUC-DER project will be able to assess the impacts of DERA on the distribution system's operation and reliability.