Distribution System Constraints Research Articles

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. This paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

Capability identification for new charging station into distribution system with electric, economic and traffic considerations

The shift of conventional vehicles to electric vehicles (EVs) is on pace nowadays. Increment in EVs adoption eventually results as increment of load on the existing power distribution system. This increment can adversely affect the performance of the distribution system. Location of electric vehicles charging station (EVCS) in the distribution system requires more technical finding before moving on into it. Implementation needs attention for several aspects like electric profile, economics, and traffic management and so, this work considers all of them. The impact of charging stations (EVCS) on operating parameters of the distribution network such as voltage stability, reliability and power losses are investigated by the proposed formulation of a Voltage Stability-Reliability-Power Loss (VRP) indices in this work. This index has the capabilities of considering voltage stability, reliability, and power loss under a single frame. The capabilities of the system with random load changes are compared with the base system without increments in load. This presented work gives assurance about the EV based load increment capabilities in the existing system without compromising with distribution system constraints. The main contribution of the proposed work is to examine the impact of voltage sensitivity, system stability, power losses, reliability measures, and a complex VRP index on choosing locations for EV charging stations and its effects on the existing system. Also, to ensure accurate results, these factors are assessed across seven distinct load scenarios, contributing significantly to the ongoing research in this field.

Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method.

Peer-to-peer energy trading under distribution network constraints with preserving independent nature of agents

In recent years, the use of distributed energy resources has accelerated to deal with global warming. As these resources are decentralized, the current centralized energy market must be modified to accommodate their characteristics. Advances in information technology have made it possible to implement peer-to-peer (P2P) markets. However, completely removing the distribution system operator from P2P exchanges can risk the network's security. Conversely, establishing network security only by the operator can decline the independence of agents. This paper proposes a decentralized framework for implementing the P2P market based on the alternating direction method of multipliers, which maintains the distribution system's constraints, considering the agents' independent nature. The agents reach convergence with minimal exchange of information while maintaining the network constraints, including voltage and current limits. The network constraints are included using a proposed sensitivity approach in the sub-problem of each agent. In this approach, by calculating the sensitivity coefficients of voltage and current, the network security can be evaluated with the changes of active and reactive powers by agents. The simulation results demonstrate that the proposed framework can efficiently maintain network constraints. The short running time of the P2P trading with the proposed framework makes it feasible for practical applications.

Distribution locational marginal price based hierarchical scheduling framework for grid flexibility from virtual energy storage systems

• VESS's are modeled using residential air-conditioners for DSM. • Local ancillary services TSO-DSO coordination scheme for cooperative management in grids. • Hierarchical framework for scheduling aggregated VESS. • Network constrained optimal VESS participation in ancillary service markets. • DSM based practical market model for SOs, maximizing overall market benefits. Maintaining generation and demand equilibrium under large scale renewable energy integration is challenging. Conventional flexibility providers such as grid-scale Energy Storage Systems (ESS) have potential but are costly solutions. With evolving smart grid scenario, aggregated demand response from Thermostatically Controlled Loads (TCLs) is utilized as Virtual Energy Storage Systems (VESS) and considered as innovative and cost efficient solution to provide fast response reserves. VESS units are often scheduled and controlled in a centralized manner considering limited distribution system constraints. However, accurate operational flexibility potential of diversified VESS units for various grid services needs to consider distribution network constraints. It can be fully utilized when Distribution System Operators (DSO) coordinate with Transmission System Operators (TSO) to transfer grid services from VESS of its area. Local ancillary service TSO-DSO coordination scheme ensures in this perspective significant flexibility of TSO and allows both TSO and DSO to access VESS flexibility. For such controlling of diversified VESS units where DSO is responsible for aggregate flexibility and local area balancing, a hierarchical scheduling framework is more practical. In this context, this paper proposes a two-stage hierarchical approach for optimal scheduling of TCLs as VESS to optimize energy and provide grid flexibility. At the lower stage, TCLs are scheduled based on Distribution Locational Marginal Price (DLMP) provided by DSO, while ensuring consumers’ comfort. At the upper level, DSO provides additional flexibility to TSO through optimal scheduling of aggregated VESS considering local ancillary services TSO-DSO coordination scheme. The simulation results indicate that VESS scheduling improves system flexibility by maintaining a generation-demand balance.

Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system

In modern power system planning and operation, the relevance of distributed power generation units and demand-side flexibility options grows. As a consequence, smart planning and operation routines become necessary also in the distribution grid. We therefore develop a co-optimization framework to consider transmission and distribution system constraints and their interactions together. This enables bidirectional flows between both systems, supporting a consistent and cost-efficient overall energy system. The key feature of this paper is an automated coupling of two independently and modularly defined systems. It allows a bottom-up modelling approach of energy demands, conversion and storage technologies from both system levels. However, this approach increases the model size significantly. Thus, timeseries aggregation methods are applied to reduce the computational complexity. The results for the optimal planning of the coupled German energy system show that the electrification of heat and mobility sectors in the distribution grid is not only beneficial to achieve carbon neutrality in 2050, but also to particularly make use of additional flexibility potentials. Our results underline a significant positive correlation between a smart operation of heat pumps and charging stations with the maximum integrable capacities of low-cost rooftop photovoltaics in the distribution system.

Electric vehicle charging management using location-based incentives for reducing renewable energy curtailment considering the distribution system

There have recently been an increasing number of variable renewable energy curtailment cases to manage power system stability problems caused by the overproduction of variable renewable energy. Variable renewable energy curtailment can be a factor that hinders the development of the power industry by reducing the economic viability of variable renewable energy operators and the efficiency of energy use. In this study, an economic curtailment methodology using electric vehicles to mitigate energy waste and economic losses is proposed. Charging point operators participate in incentive-based demand response programs with their charging station loads under curtailment situations. A location-based incentive algorithm considering distribution system constraints is proposed to avoid distribution system congestion. A response model based on random arrival electric vehicles considering demand and price elasticity, time pressure, and charging pressure is developed. The proposed methodology is verified through case studies based on the environment of Jeju Island in Korea. The case study results show that the proposed methodology can use energy more economically and effectively than conventional variable renewable energy curtailment methodology.

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

Increasing penetration of distributed renewable energy sources (DRESs) has resulted in the emergence of distributed energy resource aggregators (DERAs). A DERA participates in the transmission-level market operated by a transmission system operator (TSO), and the DERA's resources are connected to a jurisdiction of the distribution system operator (DSO). Inspired by the structure of the Korean power industry, this study assumes a minimal DSO that cannot directly dispatch the resources in its system. In this study, we develop a detailed procedure for prequalification wherein the DSO checks the DERA's bids that are submitted to the TSO markets. The proposed prequalification enables the DSO to secure the reliability of its system by providing limited network information to the DERA. The DERA modifies its bid until potential overvoltage and overflow problems are resolved, even in the worst case, including uncertainties. The proposed prequalification process is verified using the IEEE 33-bus distribution network. Compared to previous studies, the results demonstrate that the proposed prequalification can deal with distribution system constraints, even though uncertainties are included. The proposed prequalification process can be applied to power industries where the DSO does not have the full dispatch authority on DRESs.

Constrained Thompson Sampling for Real-Time Electricity Pricing With Grid Reliability Constraints

We consider the problem of an electricity aggregator attempting to learn customers' electricity usage models while implementing a load shaping program by means of broadcasting dispatch signals in real-time. We adopt a multi-armed bandit problem formulation to account for the stochastic and unknown nature of customers' responses to dispatch signals. We propose a constrained Thompson sampling heuristic, Con-TS-RTP, as a solution to the load shaping problem of the electricity aggregator attempting to influence customers' usage to match various desired demand profiles (i.e., to reduce demand at peak hours, integrate more intermittent renewable generation, track a desired daily load profile, etc). The proposed Con-TS-RTP heuristic accounts for day-varying target load profiles (i.e., multiple target load profiles reflecting renewable forecasts and desired demand patterns) and takes into account the operational constraints of a distribution system to ensure that customers receive adequate service and to avoid potential grid failures. We provide a discussion on the regret bounds for our algorithm as well as a discussion on the operational reliability of the distribution system's constraints being upheld throughout the learning process.

Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management

The importance of distribution system operator (DSO) continues to increase owing to the widespread use of distributed energy resources (DERs) in power distribution system. A virtual power plant (VPP) that mostly relies on small DERs tends to make operations more difficult for DSO, because the VPP attempts to maximize its profits regardless of the status of the corresponding distribution system if an appropriate cooperation mechanism is not present. In this study, an interaction-based VPP operation methodology using distribution system constraints (DSCs) is proposed for DSO voltage management, assuming that the VPP primarily participates in the wholesale energy market (WEM). For efficient cooperative operations between a DSO and VPP, the VPP's DERs are grouped according to the DERs locations in distribution lines. Then, distributed system constraints that represent appropriate power outputs for the specific resource groups are calculated through the interactions between the DSO and the VPP. Both Volt-Watt control (VWC) and Volt-var control (VVC) are considered for optimal DSC calculation. The VWC and VVC-based operation results are compared to evaluate the technical and economic effects from both the DSO and VPP perspectives. The case study results show that the proposed methodology keeps the distribution system's voltage within the target range, reduces economic losses by approximately 24% compared to traditional methodologies, and reduces the DSC calculation rate of DSOs by approximately half. These results indicate that the proposed methodology could be effectively utilized in DSO and VPP operations.

Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

In this paper, improved single- and multi-objective Harris Hawks Optimization algorithms, called IHHO and MOIHHO, respectively are proposed and applied for determining the optimal placement of distribution generation (DG) in the radial distribution systems. Harris Hawks optimizer (HHO) is a new inspired meta-heuristic optimization technique that is mainly based on the intelligence behavior of the Harris hawks in chasing prey. The IHHO and MOIHHO are applied for determining the optimal size and location of DG at different operating power factors (p.f) with the aim of minimizing the total active power loss, reducing the voltage deviation (VD), and increasing the voltage stability index (VSI) considering the operational constraints of distribution system. In IHHO, the performance of the conventional HHO algorithm is improved using the rabbit location instead of the random location. In MOIHHO, grey relation analysis is applied for identifying the best compromise solution among the non-dominance Pareto solutions. To verify the effectiveness of the proposed algorithms, IEEE 33-bus and IEEE 69-bus radial distribution systems are used, and the obtained results are compared with those obtained by other optimization techniques. The results prove the efficiency of the proposed algorithms in terms of best solutions obtained so far for the single- and multi-objective scenarios.

Secure and Reliable Distribution Feeder Reconfiguration in the Presence of Static VAR Compensator

This paper presents a multi-objective approach for solving distribution feeder reconfiguration (DFR) problem aiming at optimizing active power of distributed generations (DGs) and locating static VAR compensators (SVCs) in distribution network. To have a positive effect, DGs and SVCs must suitably integrate and coordinate with the distribution system feeder design. Besides the power loss reduction as a main purpose, expansion of distribution systems forces the system operators to obtain an acceptable reliability and the voltage stability level in distribution system. Furthermore, increasing DG penetration in distribution systems pushes system operators to take transient stability of DGs into account. Considering multiple objective functions beside different operational constraints of distribution systems makes the proposed DFR problem a complex optimization problem that needs to be solved by a proper solution methodology. In this connection, teacher learning-based algorithm, a well-known evolutionary algorithm, is employed to solve the proposed problem. The proposed optimization algorithm is coded in DPL environment of DIgSILENT® Power Factory with a detailed dynamic modeling of all electric devices. The effectiveness of the proposed approach is demonstrated using a typical 84-bus test system.

Scenario-based planning of fast charging stations considering network reconfiguration using cooperative coevolutionary approach

Abstract In recent years, plug-in electric vehicles (PEVs) have attracted a great deal of attention because of environmental issues. For large-scale integration of PEVs, appropriate siting and sizing of PEV charging stations are essential. In this paper, a framework for simultaneous fast charging stations (FCSs) planning and distribution network reconfiguration is proposed. The distribution network reconfiguration is effectively used to optimize objective functions. The objective function considered is defined as the sum of the initial investment and energy loss costs subject to different distribution system constraints. In the model, a scenario-based approach is used for taking into account uncertainties of traditional electric load, FCS load, and electricity price. To reduce the computational burden of the problem, a scenario reduction technique is utilized. A cooperative coevolutionary genetic algorithm (CCGA) is employed to solve the problem. The FCSs locations and the network switches are coded separately, and they evolve cooperatively as two different species, i.e., the location species and the switch species. Furthermore, an efficient technique called Dijkstra's algorithm is used to calculate FCSs sizes in which the nearest FCS to PEVs is determined based on their driving distance from FCSs on their path to their destination. Finally, the performance and the effectiveness of the proposed method are demonstrated by numerical results. More specifically, use of the proposed scenario-based approach yields to a more accurate solution than deterministic method. In addition, usage of network reconfiguration in FCSs planning both reduces the total costs and improves the voltage profile.

Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems

Although future widespread use of plug-in electric vehicles (PEVs) will considerably reduce the greenhouse gas emissions, their charging demand will pose significant challenges to the grid. Aggregated charge scheduling of PEVs in parking lots (PLs) can mitigate the challenges, if the size and place of the PLs for PEVs are optimally determined. This paper proposes an optimization-based problem to not only determine the optimal size and place of PLs for PEVs, but also optimally schedule the charge and discharge of PEVs in the located PLs. The operation costs of distribution system, including the cost of purchasing energy from upstream grid and the cost of PEV charging in PLs, are considered as the objective of the proposed optimization problem, subject to the distribution system constraints. The proposed method is examined in a test system, considering different penetration levels for PEVs. In addition, the impact of results is investigated on the reliability of distribution system. The results show that decrements in the total cost of operation may lead to a lower reliability for the distribution system. The sensitivity of the results to the maximum charging rate of the parked PEVs in the located PLs is also reported in this paper.

Robust operating zones identification for energy storage day-ahead operation

Abstract This paper presents a tool to robustly allocate the allowable operating zones of active and reactive power margins for multi-energy storage systems (ESSs) without violating typical distribution system constraints. This tool helps ESSs to manage its day-ahead energy independently without violating the power system constraints. The main contribution is considering the power uncertainties (loads and renewable energy) without taking very conservative decisions. For defining a robust operating zone (ROZ) for ESSs; first, a particle swarm optimization (PSO) algorithm detects the day-ahead worst-case power flow due to loads, renewable energy sources (RESs), and contingency uncertainties. Second, a second-order cone programming problem (SOCP) optimizer determines the maximum charge and discharge active powers for each ESS that maintains safe system operation limits (voltage limits, cables capacity, and reverse power flow limits). While an uncertainty budget designed by the fuzzy expert, the PSO uses this budget to reshape its searching space with less conservatism. Case studies with one hundred different uncertainty scenarios are conducted on a real 41-bus Canadian system. Simulation results have shown that the proposed algorithm provides robust operating zones for ESSs that consider uncertainties with reducing conservatism.

Volt/VAr Optimization of Distribution System with Integrated Distributed Generation

This paper addresses the issues of VVO (Volt/VAr Optimization) such as loss minimization, acceptable voltage profiles and optimized number of switching operations. Basic function of the DMS (Distribution Management System) is to upgrade system intelligence so that it can make dynamic decisions and control the network in realtime. Distributed generators can cause the system to operate above and below the desired limits due to their variable nature. Therefore, devices like SC (Shunt Capacitors) and OLTC (On Load Tap Changers) are used in distribution system as control devices. Main focus of this paper is to inspect effects of DG (Distributed Generation) on switching states of control devices while considering Volt/VAr standards. An optimization search algorithm is employed to search the optimal solution considering the system constraints. The GA (Genetic Algorithm) is used for the optimization process of the system and the simulation is done in MATLAB using IEEE-30 bus system with DG under 24 hour changing load profiles. By setting up constraints of distribution system’s voltage limits, capacitor bank and OLTC, losses are minimized up to 50%. Merits of the proposed optimized method are demonstrated through simulation results .The result achieved from the proposed technique has proven to be beneficial for switching optimization of control devices under variant conditions of loads and distributed generation

Two-Stage Residential Energy Management Considering Network Operational Constraints

This paper suggests a decentralized optimization of residential demand to minimize the cost to the utility company and customers while maintaining customer satisfaction. As an important feature, distribution system constraints such as equipment capacity, line flow limits, and phase balance constraints are taken into account. Due to the large amount of uncertainty in the residential demand, a two-stage pricing is considered. In the first stage, day-ahead prices as well as residential appliance scheduling are determined through interaction of the utility company and residential customers. Uncertainty of the customer demand is also considered in the day-ahead pricing. In the second stage, on the other hand, prices will be updated based on the actual residential loads. Similar to locational marginal price (LMP) in transmission system, customers on different nodes are charged with different prices. Performance of the approach is shown on a sample distribution system.

Distribution Systems Reconfiguration Using Ant Colony Optimization and Harmony Search Algorithms

One objective of the feeder reconfiguration problem in distribution systems is to minimize the distribution network total power loss for a specific load. For this problem, mathematical modeling is a non-linear mixed integer problem that is generally hard to solve. This article proposes two heuristic algorithms inspired from natural phenomena to solve the network reconfiguration problem: (1) “real ant-behavior-inspired” ant colony optimization implemented in the hyper cube framework and (2) the “musician behavior-inspired” harmony search algorithm. The optimization problem is formulated taking into account the operational constraints of distribution systems. A 32-bus system and a 118-bus distribution were selected for optimizing the configuration to minimize the losses. The results of reconfiguration using the proposed algorithms show that both of them yield the optimum configuration with minimum power loss for each case study; however, the harmony search required shorter simulation time but more practice of the iterative process than the hyper cube–ant colony optimization. Implementing the ant colony optimization in the hyper cube framework resulted in a more robust and easier handling of pheromone trails than the standard ant colony optimization.

Usefulness and limitations of decision support systems for improving irrigation scheme management

SIMIS (the FAO Scheme Irrigation Management Information System) is a decision support system that integrates tools and performance indicators to facilitate the planning and management of irrigation schemes. The authors used SIMIS to compute performance indicators in an irrigation scheme in Southern Spain that were used to identify distribution system constraints affecting the flexibility of water deliveries and to identify scheme sectors where deliveries could not meet the predicted crop water demands. Applying SIMIS, the authors and the irrigation scheme manager evaluated measures to overcome the constraints for future irrigation campaigns, and to refine the water orders made every 2 weeks to the basin authority. On the other hand, SIMIS presented limitations to the evaluation of on-demand delivery schedules. To overcome these limitations, an external model, developed outside SIMIS, showed that the current distribution network of the scheme has the capacity to deliver water on-demand only if a slight water deficit is accepted during the peak demand period. The analysis showed that by relaxing the stringency of the quality of operation of on-demand systems, rotation systems may be transformed into on-demand systems without changing their structures. This analysis could also be done using Clément's hypothesis, but doing so resulted in overestimates of the quality of operation and of the relative irrigation supply.