Stochastic Distribution System Market Clearing and Settlement via Sample Average Approximation

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.

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 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.

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

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.

Integration of flow battery for resilience enhancement of advanced distribution grids

Optimal scheduling of smart inverter-assisted distributed energy resources (DERs) benefits power system operation and planning entities. The comprehensive evaluation and fair allocation of these co-existing benefits should be the prime criteria for designing a compensation scheme. This work focuses on classifying, quantifying, and allocating multiple benefits extracted through phase-specific optimal power injection in distribution networks. This paper proposes a fair and self-sufficient compensation scheme that stacks uniquely assigned benefits components among the DERs. Using a rectangular branch-current component-based approach, this paper proposes a method to quantify the cooperative optimal power dispatch benefits, broadly classified as capacity deferral and operational benefits. In addition, this work employs the unitary participation-based Aumann Shapley value method to allocate the joint-coalitional benefits fairly among various DERs. By implementing the proposed scheme on the IEEE European distribution network for a long time horizon, various attributes of the proposed scheme are analyzed. The proposed compensation scheme provides the distribution system operator with a financial tool to stimulate DERs' coordination in the optimal power delivery action.

Market-Based Coordination of Transmission and Distribution Network Planning with Distributed Energy Resources Planning

Smart grid is considered as next generation power system which performs automated control and monitoring operations to make it self-resilience. Advanced Metering Infrastructure is an essential part of smart grid which enables two-way communication thereby providing real-time communication link between consumer and grid operators. Emergence of Distributed Energy Resources (DERs) at end user make AMI's operation more challenging. Smart Meters (SMs) are key components in AMI that sends Power Consumption Data (PCD) of home appliances to Distributed System Operator (DSO). PCD received at DSO can be used for real time pricing applications, real time energy consumption, automatic diagnostic, daily energy metering and tasks such as billing, monitoring, planning and predicting of energy usage. In the context of selling of excess energy generated at DER's to the grid, SMs participates in Demand Response (DR) applications to perform energy trading. Revealing PCD of a home leads to privacy breach, hence it is necessary to achieve secure communication between SM and DSO. At the same time, it is essential to make energy trading transparent. To address this issue, this paper proposes a cloud based private blockchain framework to accomplish secure communication between SM and DSO without compromising privacy. Furthermore, trust between SM and DSO is achieved through smart contracts. Performance evaluation of the proposed blockchain shows its applicability to the said environment.

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

Optimal scheduling of smart inverter-assisted distributed energy resources (DERs) benefits power system operation and planning entities. The comprehensive evaluation and fair allocation of these co-existing benefits should be the prime criteria for designing a compensation scheme. This work focuses on classifying, quantifying, and allocating multiple benefits extracted through phase-specific optimal power injection in distribution networks. This paper proposes a fair and self-sufficient compensation scheme that stacks uniquely assigned benefits components among the DERs. Using a rectangular branch-current component-based approach, this paper proposes a method to quantify the cooperative optimal power dispatch benefits, broadly classified as capacity deferral and operational benefits. In addition, this work employs the unitary participation based Aumann-Shapley value method to allocate the joint coalitional benefits fairly among various DERs. By implementing the proposed scheme on the IEEE European distribution network for a long time horizon, various attributes of the proposed scheme are analyzed. The proposed compensation scheme provides the distribution system operator with a financial tool to stimulate DERs’ coordination in the optimal power delivery action.

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.

Smart distribution system management considering electrical and thermal demand response of energy hubs

In this paper, a planning model for active distribution network (ADN) is proposed considering distributed energy resources (DERs) and distribution network expansion planning (DNEP) to cope with load growth and to maximize profits to the distribution system operator (DSO). The DERs considered in this paper include distributed renewable energy resources (RESs), distributed thermal generators (TGs), and battery energy storage system (BESS). DNEP options, such as adding new lines to the distribution network (DN) and upgrading existing lines are taken into account. The main motivation of this research is to find the optimal sizing and siting of DERs with considering DNEP to meet the load growth requirements and to maximize the total payoff to the DSO. The DSO trades energy externally with an independent system operator (ISO) at the market-clearing price (MCP) and internally with consumers at a predetermined cost. The overall performance of the proposed model is investigated on a 38-bus radial distribution test system. Simulation results indicate that considering DERs contributes to maximizing the total payoff to DSO and meeting the load growth requirements. In addition to that DERs can relieve the congestion in the DN's lines; therefore, it can mitigate or postpone DNEP's investment.