Computational Optimization of Energy Storage System in Managing Distribution Feeder Overloading

Impacts of optimal energy storage deployment and network reconfiguration on renewable integration level in distribution systems

The necessity for Renewable Energy Resources (RES) has become a major issue due to the shortage of adequate fossil fuels recently. In addition to fossil fuel shortages, global warming is also an anxiety for a lot of countries and companies. These problems have led to the addition of a large number of renewable energy sources to modern distribution systems. However, due to the high integration of RES to these systems and the intermittent characteristic of sources such as wind and solar, it causes variable production and uncertainty in the power system. It is proposed as a solution to gather these resources together in order to solve the problems caused by the changeable outputs of these resources. Virtual Power Plant (VPP) is described as the management of distributed generation facilities, energy storage systems and controllable loads by an Energy Management System (EMS). In this study, the VPP model consists of renewable energy sources such as wind power plant (WPP), solar power plant (SPP), biogas power plant (BPP) and pumped hydro storage plant (PHSP) as an energy storage system (ESS). The aim of the VPP is to maximize the net daily profit in a 24 h time interval. The proposed mixed integer linear programming (MILP) optimization problem is modeled in GAMS software and solved using CPLEX.

In the current study, the costs and benefits of deploying energy storage system (ESS) are discussed, and the role of ESS in transmission expansion planning (TEP) is investigated. Based on the classical formulation of TEP, a new formulation is developed considering the simultaneous addition of new circuits and installation of ESS. The fictitious costs of ESS are set instead of actual costs in the proposed model, which can be used to determine the site and size of ESS for transmission investment reduction. The whole formulation is a mixed-integer linear programming problem and can be solved by the well-developed algorithms. The proposed TEP method, which considers the optimal deployment of ESS, has been simulated on three test systems. Test results show the effectiveness of the proposed method and illustrate the potential of installing ESS to reduce network investment costs.

This paper proposes a hierarchical look-ahead framework to conduct conservation voltage reduction (CVR) when distributed energy resources such as solar photovoltaic (PV) systems and energy storage systems (ESSs), and demand response programs are integrated into distribution systems. With the increasing deployment of PV systems in distribution systems, their frequently varying power output due to cloud movements could have a detrimental impact on the consumer’s voltage quality, consequently leading to degraded CVR performance. A two-level CVR framework for the coordination of an on-load tap changer (OLTC), capacitor banks (CBs), and the smart inverters of PV systems/ESSs is presented, in which these elements operate to reduce the voltage profile along the distribution feeder at different temporal scales. At the global level, the operations of the OLTC and the CBs are scheduled every hour to achieve the best CVR performance in an optimization problem using mixed-integer linear programming. When voltage violations occur rapidly, the smart inverters of PV systems and ESSs help to maintain a lower voltage profile every second based on the proposed piecewise droop control functions at the local level. A simulation study is carried out in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and ESSs, and our results demonstrate the advantages of the proposed approach in terms of voltage level and energy savings. Furthermore, the impact of demand reduction on the proposed approach is quantified, and we verify that a higher demand reduction yields more energy savings in the proposed framework.

Vast amounts of newly-available time series measurements of electrical loads now enable data-driven optimization of energy storage systems. This paper presents an algorithm for sizing the installed capacity of an Energy Storage System (ESS) with the objective of minimizing the total energy cost for a load having a specific time series profile. This algorithm considers not only energy costs, but also demand charges, which often represent a significant portion of electric cost for commercial users. The sizing process—implemented using linear programming—takes into account ESS characteristics and costs as well as time-of-day electricity rates. This paper discusses optimization results for two case studies—a commercial property and a railroad traction substation—using the time series of hourly load profiles for one year. The algorithm successfully identified the optimal ESS capacities for both cases, yielding cost savings of 10% and 28% respectively. The algorithm also shows that a single ESS serving the aggregated loads (assuming they are co-located) would further increase the economic benefit of the ESS installation, while also being 10% smaller in capacity compared to the total sum of optimal capacities for the two individual cases. This algorithm can be used by practitioners to analyze scenarios in which two applications with different load profiles can create synergies in ESS installations, particularly for commercial businesses.

The government policies and cost benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors, challenging the electric grid's potential to maintain its stability and efficiently serve the purpose. The penetration of PHEVs brings technological advancements and environmental benefits to the traditional transportation and electrical power systems. Designing of appropriate charging methods to satisfy PHEV owners helps to improve system performance and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a coordinated charging algorithm to charge maximum number of PHEVs with the existing infrastructure at a Charging Station (CS) located in residential locality. The stochastic driving patterns of PHEVs are studied and different charging strategies are developed. The Optimized charging scheme is obtained for American Electric Power utility distribution feeder modeled in OpenDSS. Further, PHEV customers are modeled into price responsive consumers capable of participating in Demand Response programs to economically charge their vehicles and render support to the stabilization of power system. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices (PEMs) which are utilized to calculate the level of demand response.;This thesis also proposes a concept of converting the existing Personal Rapid Transit (PRT) system from guideway power rail propelling vehicles to Battery Electric Vehicles (BEVs) by developing a novel routing algorithm based on the data provided by West Virginia University's Department of Transportation and Parking. This turns into a feasibility analysis of implementing new EV technologies in electrification of public transportation. The routing algorithm involves Mixed Integer Linear Programming (MILP) Optimization resulting in hourly schedule of all the vehicles satisfying the passenger demand. The effect of charging load of BEVs on the electrical distribution system is analyzed by performing a time series simulation on AEP distribution feeder.;Additionally, the impact of PHEV charging on power market prices i.e. Locational Marginal Prices (LMP) in a distribution microgid scenario is presented. Locational marginal pricing is a market based wholesale pricing approach adopted by six out of ten national electricity markets. The uncoordinated charging of EVs results in unplanned peak demand causing system changes and fluctuation of LMP. The stabilizing effect of coordinated charging scheme is demonstrated on IEEE 13 Node test system.

ABSTRACTSoft open point‐based energy storage (SOP‐based ES) can realize the real‐time adjustment of transmission power in space and peak load shaving in time, further promoting the integration of distributed generations (DGs) and decreasing the allocation cost. This article proposed an optimal planning model for coordinated allocation of SOP‐based ES and conventional independent ES systems in PV penetrated Active Distribution Networks (ADN). First, the planning models of SOP‐based ES, battery of ES systems and its lifetime are established. Second, the proposed coordinated allocation model aims to minimize the total planning cost, including power exchange cost, investment cost, operation cost, battery degradation cost and loss cost. In order to solve the uncertainty of PV output and load demand, the stochastic optimization model is established with scenarios reduced by the K‐means clustering technique. Third, the linearization model of the battery degradation cost is derived to transform the optimization model into a mixed integer linear programming (MILP) model. Finally, the presented model is implemented on an IEEE 33‐node distribution system to confirm its effectiveness. The case study results show that the coordinated allocation of SOP and converter‐based ES can effectively reduce the allocation cost of ES and promote the integration of DGs.

The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a significant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multi-objective shuffled frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.

Energy Storage Systems (ESSs) represent one of the main vectors for improving railway electrical systems. Former low reliability rates and life-time are being dramatically improved in recent times due to the intensive research on battery and capacitor technologies. However, little research has been conducted on the optimization of railway system infrastructure by incorporating ESSs. This paper presents a method for designing the best possible deployment of ESSs in a DCelectrified mass rapid transport system line by means of optimization, selecting number and location of ESSs. A detailed model of system load flow and energy storage devices for a given traffic scenario in a general DC railway system has been carried out, resulting in a MINLP optimisation model that minimises total energy consumption. Then, a linearization of load flow equations has been performed in order to get a fast-approximate solution of the system by converting the MINLP problem into a MIP one. This optimization method has been applied to a realistic railway system, yielding reasonable location and number of ESSs in the system. The error due to the approximation has been evaluated by comparing the results of the linear load flow against those of the non-linear load flow. The method has demonstrated to be a powerful tool to decide how many and where to place ESSs in a DC-electrified railway system.

In recent years, energy storage systems (ESS) have emerged as critical components for stabilizing power grids and supporting renewable energy. The ability to optimize these systems can greatly enhance energy efficiency, performance, and cost-effectiveness. This paper explores the integration of machine learning (ML) models with Databricks, a unified analytics platform, to develop optimization strategies for ESS. Through simulation research, the study demonstrates the potential of using Databricks to forecast energy demand, predict battery degradation, and enhance storage dispatch operations. The results show significant improvements in storage system performance and suggest new opportunities for scaling ESS across different grid environments.

In this investigation, we explored the cost-effectiveness and operational efficiency of grid-connected Energy Storage System (ESS) technologies—specifically, Proton Exchange Membrane-Reversible Fuel Cell (PEM-RFC) and Li-ion Battery (LIB)—with the goal of meeting load demand. Our conclusion reveals that integrating ESS isn't the optimal solution due to impractical implementation costs, making reliance solely on grid electricity a more pragmatic choice. Despite this, a secondary optimum value emerged, suggesting economic viability for ESS integration and introducing a dynamic trade-off between cost considerations and energy storage utilization. Furthermore, our study conducted a sensitivity analysis, anchored in the target costs set by the US Department of Energy, to identify conditions under which ESS systems could transition from impractical to feasible. The results outlined the potential to reduce the levelized cost of energy (LCOE) but also emphasized the prospect of increasing the storage factor by optimizing various system characteristics. Crucial factors like capital cost, efficiency, and lifetime were identified as pivotal in shaping the economic landscape of ESS integration. The study showcased tangible outcomes upon sensitivity analysis, including a 12% reduction in PEM-RFC LCOE and 58.5% reduction in LCOE for LIB, indicating improved cost efficiency and feasibility of ESS integration to the grid. To address a critical research gap, this study provides a novel comparison between PEM-RFC and LIB technologies as ESS, considering dynamic grid electricity prices. It sheds light on intricate dynamics that must be considered in ESS deployment decisions, emphasizing the delicate balance between cost-effectiveness and operational efficiency. Focused on the electricity load demand of commercial buildings in the Santa Barbara district, our techno-economic assessment contributes valuable insights for informed decision-making in ESS integration.

Guest Editorial: Situational awareness of integrated energy systems

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.

Increased renewable energy applications in microgrids imply that there is currently less predictability in power generation, implying that there is a necessity of sophisticated methods to streamline the operations of the microgrid so that it operates effectively and steadily. In this work, a hybrid Artificial Intelligence (AI) framework is proposed to enhance the way power is transferred in AC and DC microgrids with energy storage systems (ESS), distributed energy resources (DERs) and integrated with the normal utility grid. The proposed solution comprised of DDPG Deep Reinforcement Learning and Genetic Algorithms address both real-time and global optimization issues. The DRL agent determines the optimal strategies to exploit RES, ESS and the grid and the GA enhances the initial parameters and critical elements of the model of the agent to achieve optimal performance. The framework is studied under different conditions and load and generation patterns in two simulation environments, one in MATLAB/Simulink and the other in Python during 24 hours. Compared to the outcomes, this DRL-GA approach is a far better solution than the Mixed Integer Linear Programming (MILP) and Particle Swarm Optimization (PSO), leading to an operation cost reduction, enhancing voltage stability as well as increasing renewables utilization. The findings provide a flexible and smart control strategy that will be efficient in microgrid energy management.

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