Linear Optimization Based Distribution Grid Flexibility Aggregation Augmented With OLTC Operational Flexibilities

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). Proper coordination and scheduling of these DERs requires a local retail market which can operate at the distribution grid level. In this paper, we propose a retail market for optimally managing and scheduling DERs, and coordinating ancillary services in a distribution grid. Our proposed retail market leverages a recently proposed distributed proximal atomic coordination (PAC) algorithm which has several advantages over other distributed algorithms, with reduced local computational effort and enhanced privacy. We describe how the market can be implemented using a Distribution System Operator (DSO), whose representatives are located at the primary feeder level and workers are located at the substation level, and how the DSO will interact with the Wholesale Electricity Market. Finally, we extensively validate the performance of the proposed retail market via simulations of three networks: a real distribution grid in Tokyo, a balanced IEEE 123-bus distribution grid, and a modified IEEE 13-bus network. Our results show that the proposed market is practical and can be easily implemented in distribution grids, resulting in optimal real-time scheduling of DERs and compensation in the form of distributed locational marginal prices.

The Feasible Operating Region (FOR) is defined as a set of points in the PQ plane that includes all the feasible active and reactive power flows at the Transmission System Operator (TSO)–Distribution System Operator (DSO) interconnection. Recent trends in power systems worldwide increase the need of cooperation between the TSO and the DSO for flexibility provision. In the current landscape, the efficient and accurate estimation of the FOR could unlock the potential of the DSO to provide flexibility to the TSO. To that end, much existing research has tackled the problem of FOR estimation, which is a challenging problem. However, no research that adequately organizes the literature exists. This work aims to fill this gap. Three categories of FOR estimation methods were identified: Geometric, Random Sampling, and Optimization-Based methods. The basic principles behind each method are analyzed and the most significant works involving each method are presented. For the reviewed works, we focus on the types of flexibility providing units included in the FOR estimation, the examination of time dependence, and the monetization of the FOR. Finally, the strengths and weaknesses of each category of methods are compared, providing a holistic review of the available FOR estimation methods.

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.

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

The transformation towards a sustainable power system calls for new ways of operating the network. In that regard, electric vehicles (EVs) with their charging infrastructure qualify as a flexible resource. This paper interconnects ancillary services and EV flexibility to help system operators (SOs) and flexibility providers understand the role and localize EV-chargers in the power system. First, the focus is on SOs. The manuscript reviews ancillary services based on power system operational challenges. The ancillary services are differentiated between 8 frequency and 32 flexibility services. These are then subdivided depending on the management control: the first group includes inertia, primary, and secondary/tertiary frequency control, while the second includes congestion management, voltage regulation, power quality, grid stability, and emission management. Of all the different services, the ones that can be provided by EV-charger are highlighted and classified into 12 geo-electrical charging clusters. Second, the focus is moved to the flexibility providers. Independently from location, to provide ancillary services with EVs, multiple actors are recognized: the end-user, the charging site operator (CSO), the charging point operator (CPO), the aggregator, the energy community, the distribution system operator (DSO), and the transmission system operator (TSO). The collaboration between the actors is today carried out by making alliances, to help exchange knowledge and gain confidence in ancillary services provision. In conclusion, the literature review presents the characteristics of 27 slow (up to 50 kW) smart chargers, the common flexibility features being scheduling (100%), modulation (89%), and phase switching (10%).

The increasing integration of massive quantities of generation from Renewable Energy Sources (RES) constitutes a challenge for the pan-European system. Reserve provision for the balancing of the network is an important service that, in principle, should be provided by all the units connected to the network, including RES generators themselves as well as flexible loads and storage systems. However, these resources have big peculiarities tied with their relevant technologies and could, beyond providing local services within the distribution grids (e.g. voltage regulation, congestion management), be available to provide reserve for the entire system, through the connection points to the transmission grid. This paper shows a more advanced dispatching management of distribution systems by resorting to more advanced Information and Communications Technology(ICT) systems able to transform distribution from a "passive" into an "active" system and market architectures able to incorporate scattered bids coming from distributed generation and active load. A delicate point is the interface between transmission and distribution that has to be managed in a coordinated manner between Transmission System Operators (TSO) and Distribution System Operators (DSOs) in order to achieve an overall efficiency target. ICT should also be key to allow a strict real-time coordination of the different subjects that are in charge to actuate the provision of ancillary services. Further, the paper shows as new actors could appear in the system like the "aggregators" who interface with the Market Operator to submit the bids for the provision of the services, with the local TSO to receive the consequent dispatching orders, with to the aggregated distributed resources (generation, flexible demand, storage) and with to the involved DSOs, in order to communicate how each dispatching order will in turn be dispatched among the distributed resources it aggregates.

This paper proposes an equivalencing‐tracking‐based (ETB) method to incorporate distributed energy resources (DERs) in transmission system economic dispatch (TED). This method has three key steps. The first is a ‘dispatch model equivalencing’ step in which an equivalent dispatch model is created by an aggregator or a distribution system operator (DSO) to represent the aggregate operational features of the numerous DERs on the distribution side. In the second step, the equivalent dispatch model is sent to the transmission system operator (TSO) who then solves a DER‐equivalent‐included TED problem. The optimal solution regarding the DERs' equivalent dispatch model is returned to the aggregator, who will, in the third step, solve an optimal tracking problem to provide each DER with an optimal dispatch scheme that best tracks the optimal solution regarding the equivalent model. Case studies on a real power system confirm that the transmission system operation benefits from DERs' being incorporated in TED, and also that the proposed method can attain a nearly optimal solution without repeated iterations between TSOs and DSOs, which is a notable advantage over mathematical decomposition methods.

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.

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

Today, ancillary services, such as congestion management, are mainly provided by large power plants which are located in the transmission grid. Due to the energy transition in Germany generation capacities will shift from the transmission to the distribution level. Thereby in the future the role of transmission and distribution system operators will change especially with regard to the provision of ancillary services. Thus the question is to which extent distribution system operators can actively contribute to system stability in the transmission system and how total system responsibility should be assumed by distribution system operators. This paper gives first insights about economic implications of changing roles and examines the impact of a higher degree of coordination between distribution and transmission level to provide ancillary services. Results indicate that a stronger cooperation of system operators yields positive effects for both players and substantial cost savings can be achieved.

P2P flexibility markets models to support the coordination between the transmission system operators and distribution system operators

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.

The increasing presence of Distributed Energy Resources (DERs) in distribution grids is posing relevant challenges to all electricity operators, particularly concerning the unreliability and intermittency of distributed generators based on Renewable Energy Sources (RESs). The use of Distributed Energy Storage Systems (DESSs) has been recently proposed to cope with these issues, along with the implementation of advanced Demand Response (DR) programs and of ancillary services within Smart Grids. The test of these advanced (and experimental) mechanisms in an operating distribution grid is not usually feasible, both for economic as well as for technical reasons, including security and safety aspects. Conversely, the test in simulation or in Hardware In the Loop (HIL) environments may be limiting, due to the difficulty in reproducing the typically unpredictable behavior of the power demand patterns of end-users. For this reason, a testing facility for the characterization of DESSs coupled with RES generators has been designed and deployed in the eLUX laboratory of the University of Brescia. The system is formed by a 25 kWh DESS coupled with a 64 kWp photovoltaic power plant. The testing facility has been equipped with an experimental supervisory system which allows the remote monitoring and control of both the power installations. Such a system can be used to test and validate different control approaches, including DR requests and ancillary services, under working conditions similar to the real deployment of DESSs in modern power grids.

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