Flexible Energy Systems: "Consumer centricity is the key"

The modern power system is currently undergoing a rapid transformation from centralized generation to decentralized and distributed generation. The distributed generation is connected at the distribution level and may consist of a large number of small generators with stochastic power output. Therefore, planning and operation of the future distribution grid requires new tools to ensure reliable and economical operation. One such tool is an on-line simulator. A system that uses an on-line simulator in the Energy Control Center (ECC) will enhance the operators’ situational awareness as well as operational decision making. This paper proposes a framework for an on-line, close to real time simulator, connected to a graphical user interface emulating the Energy Control Center. The framework can be used for both planning and operation of the distribution grid. It can also be extended as an interface to other infrastructure simulators.

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.

Distributed energy resources (DERs) are transforming the operation and planning of both distribution and transmission grids. Stability impacts from the increasing generation contribution of DERs have been widely recognized in distribution systems and are emerging in transmission systems. There is a need for the development of transmission hosting capacity studies of DERs so utilities will be able to predict and prevent instabilities, reductions in reliability on the electrical grid, and create planning methodologies that enable the continued growth of DERs. This paper evaluates the impact of different modeling considerations for assessing hosting capacity of DERs in transmission systems through analysis on a 2,000-bus synthetic grid test system. The modeling considerations include transient and steady state contingencies, use of dynamic load and DER models with voltage support control, dynamic load composition, and seasonal and loading scenarios. The results demonstrate that transient stability evaluations are more limiting than steady state analysis, the use of voltage support control in DERs can increase hosting capacity in the system, and large variations in hosting capacity can be found when assessing between seasonal variations. From comparing system factors the amount of wind generation in the system was a critical factor for hosting capacity in this study.

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper presents the analysis which help visualize the DER impact on the grid, identify the challenges and provides an insight into the new distribution management and control needs to enable reliable and resilient grid operations.

Electric power distribution grid operators (DSOs) are facing new requirements due to increased introduction of renewable energy, increased flexibility, and a tighter integration with customers. Although there is broad agreement that DSOs are at the heart of the transition towards decarbonized energy, there is little knowledge of what this means in terms of work system changes for the DSOs and how to approach the issue. In this paper, we present results from our work with defining and detailing organizational capabilities that enable the DSO to deliver in the future market. We find that the DSO need to professionalize its capabilities innovation, collaboration, analytics and influence because these are expected to face increasing demands. Using a participative approach as part of the re-design process was important to understand the resource development needs.

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

Coordinated optimal dispatch of photovoltaic units with updated power electronic transformer

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.

The modern power system is undergoing a rapid transformation from centralized generation to distributed generation. The distributed generation consists of a large number of small distributed energy resource (DER) based generators with stochastic power output connected at different geographic locations throughout the feeder. Therefore, the operation of the future distribution grid requires novel control strategies to ensure reliable and optimum operation. This paper presents a modified IEEE 34 bus feeder test case with over 100% DER penetration. The test case includes eight PV plants with volt-var control, two switched capacitor banks, two regulators and a battery bank. The distribution feeder test case allows for benchmarking the performance of different feeder control strategies and to conduct other DER integration studies.

Electric vehicles are seen as an option to reduce greenhouse emissions, directly related with the electricity generation mix and with the time of charging due to the variations of the generation sources during the day. At the same time, with their widespread adoption the increase in the demand for electricity to charge these vehicles could pose significant challenges to the electrical grid in terms of additional load due to unmanaged charge strategies. In order to mitigate these problems, the charging of the electrical vehicles must be managed. This paper presents the development of a system architecture to dynamically control the charging of electric vehicles to maintain the proper operation of the local distribution grid and minimize the environmental impact. The hardware consists of two modules, a meter and controllable plugs both with communication capabilities, while the software consists in a forecast and scheduler module. The forecast module calculates the load based on the power consumption behavior and uses the renewable generation forecast to assign the best time slot to charge the vehicle. The system aims to minimize the load peaks and flatten the load profile, while minimizing the environmental impacts. Based on the user preferences, system characteristics, consumption and renewable generation forecast, the system will assign the most suitable time slot to charge the electric vehicle. For the case of multiple electric vehicles, the system will schedule their charge based on a calculated priority level, in order to maintain a reliable operation of the local electrical grid.

Guest Editorial: Special issue on flexible and resilient urban energy systems

Year after year the climate change, due to global warming, is more and more tangible and brings an increasing of intensity and frequency of extreme meteorological events, such as snowstorms, windstorms, and heat waves. These events impact the operation of both distribution and transmission grids and thus affect a huge number of customers. All published projections on climate change show an increasing trend in the number and intensity of these events in the next years. Hence, these meteorological trends needs to be deeply investigated in order to reduce the impact that these extreme events will have on the continuity of the energy supply. In particular, the accretion of snow sleeves on overhead line conductors is the most impactful meteorological event. For this purpose, e-distribuzione, in collaboration with RSE, installed three experimental stations in order to acquire all necessary measures to characterize the ice sleeve phenomenon and tune properly the snow-sleeve accretion model developed by RSE for distribution grids.

Stochastic two-stage resilient scheduling of hydrogen system in power distribution grids with high penetration of solar PV systems: A convex worst case analysis

Future distribution grids are likely to shift away from a passive grid consuming power to an active grid with a high share of weather-dependent renewable generation. Distribution grid operations are greatly interlinked between different voltage levels and depend upon the fluctuating load demand. Thus it is imperative to study and analyze multi-voltage level distribution grids to understand the challenges and opportunities in a distribution grid with a high share of weather-dependent generation. In this research, an open-source multi-voltage level distribution grid model, named the DTU 7k-Bus Active Distribution Grid Model, is presented. The distribution grid model spans across three voltage levels and is modeled on geographical data for network topologies. The generation and load time-series provided with the model are simulated from weather data and derived from measurement data respectively. This work addresses key features of the model and highlights challenges due to the high share of renewables.

Despite the fact that power grids have been planned and utilized using centralized networks for many years, there are now significant changes occurring as a result of the growing number of distributed energy resources, the development of energy storage systems and devices, and the increased use of electric vehicles. In light of this development, it is pertinent to ask what an efficient approach would be to the operation and management of future distribution grids consisting of millions of distributed and even mobile energy elements. Parallel to this evolution in power grids, there has been rapid growth in decentralized management technology due to the development of relevant technologies such as blockchain networks. Blockchain is an advanced technology that enables us to answer the question raised above. This paper introduces a decentralized blockchain network based on the Hyperledger Fabric framework. The proposed framework enables the formation of local energy markets of future citizen energy communities (CECs) through peer-to-peer transactions. In addition, it is designed to ensure adequate load supply and observe the network’s constraints while running an optimal operation point by consensus among all of the players in a CEC. An open-source tool in Python is used to verify the performance of the proposed framework and compare the results. Through its distributed and layered management structure, the proposed blockchain-based framework proves its superior flexibility and proper functioning. Moreover, the results show that the proposed model increases system performance, reduces costs, and reaches an operating point based on consensus among the microgrid elements.