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An adaptive robust optimal reactive power dispatch method in unbalanced distribution networks with high penetration of distributed generation
IET Generation, Transmission & Distribution
Abstract
Due to the increasing penetration of the uncertain single-phase distributed generations (DGs), the current distribution networks are becoming more uncertain, unbalanced, and complicated than ever before, which brings great challenges for distribution network operators. This study proposes an adaptive robust optimal reactive power dispatch approach for the unbalanced distribution networks (U-DNs) considering uncertainty caused by DGs. Leveraging the reactive power compensation from inverters of DGs, the purpose of the proposed method is to minimise power losses and maintain the voltage within regulatory limits. The feasible region of DGs is estimated and considered as a new constraint, aiming to guarantee the reliable operation of U-DNs. The optimal reactive power dispatch problem is then formulated as an adaptive robust optimisation problem based on semidefinite programming. The adaptive function, which derives the relationship between reactive power and active power outputs of DGs, is utilised to make the method more flexible and less conservative. The cutting plane algorithm is introduced to solve the proposed adaptive robust reactive power dispatch model efficiently. Moreover, case studies are separately conducted on the modified IEEE 13-bus and 123-bus test systems to demonstrate the effectiveness of the proposed method.
... Reference [19] coordinately optimizes schedules of OLTC, CB, PV inverter, and distributed storage system based on the two-stage RO. An adaptive robust RPD method is proposed in [20] to reduce power losses. Compared with SP, RO can ensure operating constraints to be satisfied when uncertainties are within the predefined lower and upper bounds. ...
... However, in [15] - [20], the real-time local control is not considered or discussed. In [21], the two-stage RO is utilized to schedule the OLTCs, CBs, and PV inverters in two central control stages, while the local Q-V droop control is employed to control PV inverters in real time. ...
... Equations (12) and (19) express the maximal switch limitation of OLTC and CBs, respectively. Equations (13), (14), (20), and (21) show that the tap changes of OLTC and CBs during one operation period should be constrained within the maximum limit. Equations (15), (16), and (22) show that the total tap action times of OLTC and CBs should be limited by the allowable values during the scheduling period. ...
Photovoltaic (PV) power generation has highly penetrated in distribution networks, providing clean and sustainable energy. However, its uncertain and intermittent power outputs significantly impair network operation, leading to unexpected power loss and voltage fluctuation. To address the uncertainties, this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch (MTAAR-RPD) method to reduce the network power losses as well as alleviate voltage deviations and fluctuations. The MTAAR-RPD aims to coordinate on-load tap changers (OLTCs), capacitor banks (CBs), and PV inverters through a three-stage structure which covers multiple timescales of “hour-minute-second”. The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min. The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time. The three stages are coordinately optimized by an affinely adjustable robust optimization method. A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively. The proposed method is verified through theoretical analysis and numerical simulations.
... Ibrahim et al. [15] used chance constraints to incorporate stochastic forecasts for the generation in the network. Alternatively, Li et al. [16] dealt with the uncertainty associated with renewable generation by making their model 'robust' against perturbations in the forecasted generation. ...
... • IEEE13 appears in [16,24,25]; • IEEE34 in [24,26]; • IEEE123 in [13][14][15][16][25][26][27]. ...
... • IEEE13 appears in [16,24,25]; • IEEE34 in [24,26]; • IEEE123 in [13][14][15][16][25][26][27]. ...
This study proposes a model for multi-phase, multi-winding, lossy transformers. A methodology is developed to decompose such transformers into a sub-network of multi-conductor Π-sections, shunts and idealised (lossless) two-winding transformers. The approach, therefore, can be used to include three-phase transformer models in any unbalanced power flow or optimal power flow tool that is able to represent these three basic components. The study derives the mathematical formulation and an implementation is provided in Julia/JuMP/PowerModelsDistribution.jl. The obtained voltage profile for several distribution test cases deviates from OpenDSS by at most 4 × 10⁻⁷. A case study applies this model to optimise the tap settings in the context of conservative voltage reduction. This illustrates that the optimised tap settings can vary by as much as 30% depending on the vector group of the transformer. © 2020 Institution of Engineering and Technology. All rights reserved.
... In the case of many remote areas far from the big power grid, it is also possible to connect to the appropriate distributed power supply based on the distribution of the local natural resources, so as to solve the problem of power consumption in remote areas. Distributed electricity can be used as a back-up power supply in the event of a breakdown in the grid [8]. And when a widespread outage occurs in the grid, distributed power sources can still operate in isolation to supply power to nearby customers, reducing their outage time and serving as a necessary backup supplement to the traditional large grid [9]. ...
... As a result, the capacity of distributed power source which can be accommodated by the system will be reduced as the grid connection point moves backward under the condition that the system is not overloaded. Furthermore, the reasons which restrict the continuous growth of the distributed capacity at every node are found in the paper, as shown in Table 2. 5,6,7,8,9,10,1,12,13,14,15,16,17,18 Voltage limited 20,21,22,24,25,26,27,28,29,30,31,32,33 From Table 2, the capacity of the distributed power supply near the system bus is restricted by the restriction of the branch power, and the other nodes are constrained by the voltage restriction, so that their capacity can't keep increasing. These results agreement with the conclusion that the voltage crossing limit is the main reason for the continuous growth of the distributed power supply capacity. ...
In order to reduce energy dissipation and alleviate the energy crisis, this paper proposes an absorption mode of distribution network with distributed power supply. First of all, the starting power coefficient and cutting off power coefficient are set to reflect the efficiency of the inverter and obtain the active output power of the photovoltaic system. Then, the Gaussian iterative algorithm is used to calculate the power flow of the distribution network, which forms a model along with the branch switch to complete the variables optimization under the conditions that all power conversion elements are removed from the system and the security constraint is satisfied. Afterwards, through the apparent power of the distribution network node, the single feeder is transformed into two feeders to ensure the fixed voltage at the front end of the line and make the node voltage rise and exceed the limit. Meanwhile, the active power and reactive power are provided for distribution network to reduce the feeders voltage loss, the reasonable regulation measures are used to stabilize the voltage, and the constant power factor and voltage amplitude of the network connection point are added to address voltage over limit and lighten the influence of increasing distributed power on voltage rising. Finally, the size of voltage amplitude is referred to determine the reactive power, so as to realize reasonable absorption of distribution network. The analysis results show that the voltage regulation measures proposed significantly improve the overall absorption capacity of the power supply, with the capacity increased by 0.54MW and 0.2MW respectively.
... In [18], an optimal coordination scheme between the active power curtailment and reactive power compensation is proposed for smart PV inverters to address the overvoltage issues. Among all the above studies, the voltage droop control is still the dominant voltage support approach for smart PV inverters, while the optimal operational settings of a droop controller can be achieved using a variety of optimization methods like machine learning [19], multi-time scale collaborative optimization [20], robust optimization [21] and stochastic optimization [22]. ...
... The time resolution of the day-ahead control stage is assumed to be one hour. The corresponding control objective is to avoid any potential bus voltage violations as well as minimize the total network loss and voltage regulation costs, as in (20) and (21). The voltage regulation costs of the day-ahead control are made up of the switching action costs of OLTCs and CBs (represented by λ 2 and λ 3 in (20)). ...
With the accelerating penetration of photovoltaics (PVs) and electric vehicles (EVs), distribution networks face the risks of voltage violations and fluctuations. On the one hand, conventional voltage regulation resources like OLTC transformers and capacitor banks feature slow response and limited lifetime duration, making them incapable of quickly responding to the temporary voltage issues created by PVs and EVs. On the other hand, EVs and PVs interact with the power grid via fully controllable power electronic converters capable of real-time adjusting their operating settings, making them ideal voltage support resources. To exploit the voltage support capability of PVs and EVs, this paper proposes a two-stage control scheme for the voltage regulation of distribution networks, consisting of the day-ahead and intraday control stages. The day-ahead control mitigates potential voltage violations via day-ahead scheduling of the operation settings for OLTC transformers and capacitor banks. The intraday control further alleviates voltage deviations and voltage fluctuations based on the reactive power support of PV systems and the rational EV charging/discharging scheduling. A rolling optimization-based control technique is proposed in the intraday control stage to achieve real-time control of EVs and PVs with the stochastic nature of EV charging behaviors inherently considered. The proposed two-stage voltage regulation scheme is validated via case studies performed on the IEEE 123-node test feeder integrated with PVs and EVs.
... The introduced model reduced the system loss of the 123-bus test system by about 3.86%. Ref. [15] introduced a robust optimization procedure for optimal dispatching of distribution system resources considering single-phase distributed generations. The problem was decomposed into two subproblems, and the model considered the lower and upper bounds of optimization space using the cutting plane algorithm. ...
... Refs. [14][15] did not model the arbitrage process of distributed energy resources aggregators and the switching mechanism of the active distribution system. Based on the above categorization and for the second category of papers, Ref. [16] introduced a mixed-integer non-linear programming optimization algorithm for optimal day-ahead scheduling of distributed energy resources and switching of system switches. ...
This paper presents a two-level optimization model for the optimal scheduling of a smart grid. The smart grid operator transacts electricity and ancillary services with plug-in hybrid electric vehicle parking lot aggregators, and demand response aggregators. To tackle with carbon footprints reduction and contributing to global warming mitigation, the paper utilizes robust and lexicographic ordering optimization methods. Different case studies were carried out to assess the effectiveness of the algorithm. The proposed algorithm increases the system's revenues by about 52.09% and 47.04% concerning the case without the proposed method, respectively.
... The optimisation approach is a convex optimum power flow (OPF) based on benders decomposition. Simulations are carried out for a single load level; in [8], the authors apply the cutting plane algorithm to solve an adaptive single-objective optimisation problem. In view of photovoltaic (PV) uncertainty, reactive power from inverters is managed to reach minimum power losses. ...
... Once all steps are evaluated, it is possible to calculate the fitness function F i , given in (8), that measures the quality of the solution x i . Here, the mathematical formulation is manipulated to deal with the non-convex inequality constraint of voltage unbalance ...
Distributed generation (DG) and other electric resources such as batteries and electric vehicles are transforming the planning and operation of power distribution system all over the world. Although the operation gets more complex in the presence of DG, it also brings some potential benefits to the grid. In this study, the authors propose an optimisation approach for multiple DG units scheduling, considering a daily load profile. The main objective is to minimise the total energy loss in a period of time, dealing with a specific voltage and unbalance constraints, required by the Brazilian Electricity Regulatory Agency. The problem formulation results in a discontinuous non-convex objective function. An empirical continuous metaheuristic (ECM) is proposed to solve this challenging optimisation problem. As metaheuristic methods are suitable for this kind of problems, they present some limitations regarding final results variability, relative dependence on initial conditions and usually a large set of parameters to tune. ECM confronts directly these limitations, presenting good quality results in comparison to other well-known algorithms. By using the Open Distribution System Simulator - OpenDSS, and the well-known IEEE-123 distribution system, the proposed approach shows its effectiveness and efficiency to optimise the grid operation, with special attention to the Brazilian requirements for unbalance.
... To an increasing degree, practical electric grids contain at least some imbalance. This is often due to increasing numbers of distributed generation sources [14]. The injected instantaneous power to the grid under unbalanced grid conditions contains an oscillatory term in combination with a dc component. ...
... The expression in (14) shows that the dc-link voltage contains only even harmonics when output current and voltage components contain only odd harmonics. ...
Converters are playing an ever increasing role in electric power grids. To help facilitate a design of power grids that ensures stable and efficient transfer of power, the harmonic pollution injected by converters must be quantified. At present, analytic expressions used to describe modulated converter waveforms have been derived while assuming that the converter dc-link voltage contains only a dc component. This is a reasonable assumption in many converter applications. However, there is a large number of converters that operate with superimposed oscillatory dc-link components, such as single-phase and cascaded multilevel converters. Grid and/or load imbalance can also lead to oscillatory dc-link voltage components. Given this context, the main contribution of this paper is to derive analytic expressions for converter output harmonics when oscillatory components are present in the dc-link voltage. Experimental verification of the proposed spectral analysis technique is performed on a 1kva, 3-level, 240V single-phase full-bridge inverter.
... However, optimisation of ADN subjects to the following obstacles. On the one hand, the single-phase DG integration in reality contributes the unbalanced circumstance in ADN, where unequal single-phase loads and non-equilateral conductor spacings of distribution lines should also be considered [14]. On the other hand, since resistance is unable to be neglected compared with reactance, active and reactive power coupling characteristics shape another problem [15]. ...
... For utility grid, (1)- (8) and (16) constitute the utility grid optimisation model. For each VPP, (1)- (14) and (17) formulate the VPP optimisation model. As illustrated in Fig. 2, utility grid and VPP optimise their local models with the coupling consensus constraint of the boundary interface. ...
To manage and control massive distributed energy resources (DERs) integrated into active distribution networks (ADNs), dispersed DERs are aggregated as virtual power plants (VPPs). This study introduces a distributed optimal dispatch strategy of VPPs considering operational constraints in unbalanced ADN. First, a practical multi‐VPP coordination optimal dispatch model considering P–Q coupling characteristics in unbalanced ADN is proposed. Then, the primal–dual interior‐point algorithm is employed to transform the original model into a Karush–Kuhn–Tucker equation, where afterwards distributed quasi‐Newton method is developed to solve the equation. Under the proposed distributed strategy, each VPP only communicates limited boundary information with utility grid. With the advantage of quasi‐second‐order information, the proposed method outperforms the popular distributed gradient‐based algorithm, alternating direction method of multipliers. The distributed method provides merits such as efficient calculation and privacy protection for multi‐VPP operation in ADN. Numerical tests evaluate the efficiency and accuracy of the proposed method.
... These include "Stochastic Optimization of Distribution Networks Using Bayesian Networks" and "Markov Random Fields for Probabilistic Modeling of Distribution Networks." [19] These papers show how probabilistic graphics models, like Bayesian networks and Markov random fields, can be used to show the unknowns that come with running a distribution network [11]. Researchers have shown that these models work well for improving system performance by taking into account the statistical relationships between factors. ...
In electricity grid management, optimizing distribution networks is a must for making sure that the grid is reliable, efficient, and resilient. Stochastic optimization methods have become very useful for dealing with the unknowns that come up in grid operations because of things like adding green energy, changing demand, and broken equipment. We present a new way to improve distribution networks when there is doubt in this study. It uses probabilistic graphical models (PGMs). Using PGMs lets us describe the complicated connections between loads, producers, and grid infrastructure, as well as the relationships between these parts of the distribution network. By recording these relationships, we can accurately show how unclear the grid is and make smart choices to make it work better. In particular, we use Bayesian networks (BNs) and Markov random fields (MRFs) to describe how the different factors in the network are likely to be related to each other. We show how well our method works by using it on a real-life delivery network problem. We look at a case study of a distribution network that has a lot of green energy sources and changing load levels. We use PGMs to build a statistical model of the distribution network by combining past data, weather forecasts, and real-time measures. Then, we create a stochastic optimization problem to find the best way to reduce the predicted operational cost while still meeting different operational restrictions, like voltage limits, power balance, and equipment limitations. We use advanced optimization algorithms, like stochastic gradient descent and genetic algorithms, to quickly solve the optimization problem that was given. We show that our method works and can be scaled up for handling distribution networks when there is doubt by doing a lot of computer tests and risk analyses. The suggested method can make delivery networks much more reliable, cost-effective, and resilient than traditional linear optimization methods, as shown by our results. Overall, this study shows that probabilistic graphical models can be a very useful tool for managing the electricity grid and finding the best ways to use distribution networks in the face of randomness. Including unknowns in the modeling process helps we make stronger and more dependable choices that will help current distribution systems work well.
... References [1][2][3][4] first defined the sources of DG and then stated the reasons for using and replacing these sources instead of traditional power plants from the point of view of economic and environmental benefits. References [5,6] have also dealt with the proper utilization of distributed renewable production resources in MGs. ...
In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. The operation time of the relays depends on the short circuit current passing through them, so it is necessary to calculate the network variables before the fault occurs. For this purpose, optimal daily power distribution should be used in the micro-grid, because micro-grids consist of storage and renewable resources. The proposed plan includes the uncertainties of consumption and generation capacity of renewable resources. Then, to achieve a reliable answer with a low standard deviation, the refrigeration optimization algorithm is used to solve the proposed problem. Finally, the proposed design is implemented on the standard test system in the MATLAB software, and then the capabilities of the proposed design are examined.
... Since the PDF is obtained based on the mean values of the uncertain parameter over a long time horizon, it is challenging for the PDF to cover the entire range of uncertainty [23]. SP-based methods usually have the additional drawback of heavy calculation burdens [24]. The IGDT is a non-probabilistic method without strict statistical assumptions and is used when the PDF of uncertain parameters is difficult to obtain [25], or there is no access to historical data on stochastic parameters. ...
Abstract The Volt/Var optimization (VVO) problem is used for scheduling the voltage regulation and reactive power compensation equipment in distribution networks to minimize power loss and voltage violation. In order to solve the VVO problem for a forthcoming time horizon, it is necessary to predict some parameters such as load demand and renewable energy production. The prediction of these parameters is always accompanied by uncertainty that robust optimization can be used to solve this concern. This paper presents a scenario‐based robust Volt/Var optimization (RVO) method that significantly reduces the number of scenarios required for the worst‐case approach. Solving the VVO problem with the worst‐case scenarios reduces the computational burden and maintains the voltage security of the distribution network against the severe events. The proposed RVO is formulated based on a mixed‐integer second‐order cone programming (MISOCP) model in which Volt/Var control (VVC) equipment is scheduled over a two‐stage strategy. The proposed method is validated using modified 33‐bus and 69‐bus IEEE test systems. The results demonstrate that the proposed RVO method maintains the network's voltage profile within the acceptable range against uncertainties.
... Under the worst conditions, it may lead up to the DGs out of service and destruction of electric equipment, which is a severe waste of renewable energy and power grid assets (Tonkoski et al., 2012;Eftekharnejad et al., 2013;Gao et al., 2018;Zhang et al., 2019). On the other hand, the integrated inverter-based DGs are excellent active and reactive power supply resources with a fast response speed; thus, it promotes the controllability and optimal operation potential of DN significantly (Li et al., 2018). ...
Facing the high proportion of distributed generations incorporating in a single phase, the active distribution network has become more unbalanced with flexible topology. In this paper, a unified active and reactive power coordinated optimization (ARPCO) method, which is applicable in both radial and looped unbalanced distribution networks, is proposed. Aiming to reduce power losses and restrain undervoltage and overvoltage problems, the ARPCO model which regulates the active and reactive power output of distributed generations coordinately and optimally is constructed. A novel trust region sequential linear programming (SLP) method, which is effective in nonlinear and nonconvex model solving, is developed and employed in APRCO model solution. A multi-scenario case study based on the modified IEEE 123 node distribution system shows that the proposed method is able to reduce the system active power loss and solve undervoltage and overvoltage problems efficiently, at the same time maximizing the utilization of distributed generations.
... However, they create some new problems in the operation of distribution systems. For instance, they may reverse power flows and disrupt the protection scheme of distribution networks; they may worsen power quality, and they may increase three-phase unbalancing factors in unbalanced distribution networks [2]. ...
Renewable Energy Sources (RESs) are increasingly integrated into distribution networks due to their undeniable technical and environmental advantages. Despite all the economical and environmental benefits of RESs, they can negatively affect the distribution network. For instance, their output generations are not predictable, and these uncertainties will lead to some operational challenges. Also, RESs are affected by the climate situation, and there are high correlations between them, generally. The correlations between RESs intensify the operational challenges of energy systems. Soft open points (SOPs) are flexible power electronic devices that can effectively increase the efficiency of energy systems. They realize accurate active power control and reactive power compensation to reduce power losses and adjust three-phase voltages. This paper focused on the optimal determination of the location and setting points of SOPs in unbalanced distribution networks in the presence of correlated uncertain sources. The genetic algorithm (GA) was used to solve the main optimization problem, and the correlation between uncertain sources was managed by the Nataf transformation technique. An IEEE 37 bus test system was utilized to illustrate the effectiveness of the proposed method.
... Photovoltaic (PV) power generation and fuel cell connect to the grid through the inverters, while micro gas turbine (MT), biomass power generation system and wind power generation (WG) are directly connected to the grid in the form of adjustable excitation voltage synchronous generator. The DGs all have certain reactive power capacity, which can not only transmit active power to the power grid, but also transmit a certain capacity of reactive power [17]. Besides, based on the characteristics of inverts, some DGs can operate in four quadrants, which can generate/absorb active and reactive power. ...
Abstract In order to solve the coordinated optimization problem of distributed generation (DG), on‐load tap changer (OLTC) and capacitor banks, a dynamic reactive power optimization (DRPO) method based on fuzzy time clustering is proposed. In the method, fuzzy time clustering algorithm is used to cluster the static optimal switching sequence of OLTC and capacitor banks. The time decoupling of control variables is realized and it meets the constraints of the maximum switching times. Next, the influencing factors of DG reactive power limit are analysed. Considering the control ability of OLTC, capacitor banks and DG in voltage and reactive power optimization, a coordinated optimization method is proposed. Since the adjustment of OLTC tap position directly affects the voltage and reactive power distribution of the whole line, the switching time and tap position of OLTC are determined based on the static optimization results of each time and fuzzy time clustering algorithm. Then, the switching time of capacitor banks is determined by fuzzy time clustering algorithm. At last, the switching capacity of capacitor banks and DG reactive power are jointly optimized to get the final control scheme. The proposed method demonstrated good performance in the modified IEEE33 node system and two actual power grid examples.
... At the same time, the ADN can also control the parallel capacitor switching and the on-load tap changer taps in real time and accurately. Yue Yang and Peishuai Li [10,11] proposed an adaptive robust reactive power optimization model for the unbalanced distribution network caused by distributed generation (DG) power, which alleviated the overvoltage problem and reduced the control cost. However, this model does not consider the coordination of the DG with capacitors, transformers and other equipment. ...
The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.
... The worst-case scenario defined is that given a feasible solution, and then finding a set of parameters from the uncertainty set, the objective function value is the worst. Because each feasible solution corresponds to a function value, it can be regarded as a function that takes the feasible solution as its independent variable, and then the optimal value of this function is solved [8][9]. It can be seen from this that as long as the uncertain parameters are perturbed within the defined uncertainty set, the optimal solution will not fluctuate, which proves the robustness of the model. ...
Traditional optimization for distribution network usually uses reactive power optimization to ensure the safe operation of the distribution network, and uses the distributed power and various adjustable resources of active power adjustment to ensure the economic operation of the distribution network. However, in the distribution network, the impedance ratio of the line is large and the coupling of active and reactive power is strong. Unilateral active or reactive optimization for distribution network is not comprehensive. In addition, the uncertainty of Distributed Generation and load poses a great challenge to optimization for active distribution network, the control strategy formulated by traditional deterministic optimization might inaccurate. In this paper, the mathematical model of active and reactive power coordinated optimization for active distribution network is given. Various algorithms dealing with the uncertainty of distribution are introduced, and advantages and disadvantages of various algorithms are analysed.
... To reduce the environmental pollution caused by fossil fuels, conventional energy sources are increasingly being replaced by renewable energies. The penetration of renewable energy sources has greatly accelerated in recent years [1,2]. Microgrid (MG) represents an autonomous small-scale system which collects distributed power generations and loads. ...
The optimal dispatch of microgrids is greatly challenged by various uncertainties, introduced by dynamic power fluctuations in loads and variations in renewable energy sources. To depress the over‐conservativeness of traditional robust optimisation, this study puts forward a two‐stage coupled robust and stochastic optimal (CRSO) dispatching model with mixed integer recourse for the islanded hybrid AC/DC microgrid. The CRSO model comprehensively considers the uncertainties in source–load power and bidirectional converter overhauling, and the stochastic source–load uncertainty sets are constructed by interval partitioning methods. The CRSO model optimises the binary states of the diesel generator and the bidirectional converter in the first stage, and the second stage obtains the flexible operating states and power of each unit. The daily worst‐case operation cost is minimised by the nested column‐and‐constraint generation algorithm. Numerical simulations have validated the rationality and superiority of the developed dispatching model and its solution method.
... The distribution power network is at the end of the power system and directly connected with users. It has the characteristics of wide geographical distribution, large scale of power grid, multiple types of equipment, multiple network connections, and various executing modes [1]. With the advancement of power informatization, the scale and type of distribution power network data have increased rapidly [2], [3]. ...
Aiming at the problem of lacking efficient distributed fault tolerant mechanism for data explosion in the distributed distribution power automation system, based on the record-level fault tolerance of resilient distributed dataset(RDD) and micro-batch computing, a new lineage chain mark(LCM) faulttolerant method is proposed. Used the distribution power network monitoring dataset as cache object, the programming model based on the micro-batch processing framework is comprehensively used to design the topology example of the distribution power network monitoring data, which can shorten the derivative chain of RDD. This paper takes the monitoring data in a dispatching and monitoring system of Shijiazhuang-Dezhou distribution power network as data source, which has deployed a flow calculation test cluster of 1 primary node and 3 working nodes. Taking the most frequent fault of single data node failure condition as an example, the test results based on 3 millions monitoring data in RDD show that, the CPU usage volatility of this model in cluster nodes was less than 1.5%, the network consumption of read and write execution decreased by about 3.1% and 5.0% respectively compared with the original model, and the disk usage was reduced by 4.2%; The iterative test shows that the number of iterations is positively correlated with the computational time, which is consistent with the calculation formula for the worst execution time of the lineage chain labeling model. When the number of iterations is more than 400, the greater the number of iterations, the more significant the LCM method improves the performance of the calculation. The results show that the long pedigree chain can be cut off by using the pedigree chain marker sequence, so that multi-computer parallel processing can be carried out in the nodes, which reduces the computational overhead of iterative operation and effectively improves the performance of fault-tolerant computing.
The local energy market (LEM) has recently been introduced to facilitate peer-to-peer (P2P) energy trading within an integrated energy market. Nevertheless, sharing energy can result in profit loss for participants, reducing their incentive to join the coalition. This chapter introduces a trading mechanism for the market organizer to allocate cooperative surplus based on the market contribution of each participant. This model ensures key market mechanism attributes: social welfare maximization, individual rationality, cost recovery, and incentive compatibility, allowing participants to receive their fair share and avoid profit reductions. Additionally, the unpredictable and intermittent nature of power outputs from distributed renewable energy sources presents a significant challenge for organizing a LEM. Thus, an adaptive robust optimization (ARO) algorithm is used to identify scenarios that enhance conservativeness and economy, improving the efficiency of the market-clearing process. We transform the original problem into a tri-level model and solve it through iterative internal and external formulations. Numerical results demonstrate a social welfare increase of $2751 compared to the independent mode, with a 71.55% improvement in calculation efficiency using the ARO algorithm.
Aiming at the uncertain optimization problem of AC/DC hybrid distribution network under the coordination of source, grid load and storage, an AC/DC hybrid distribution network operation scheme based on two stage affinely adjustable robust optimization was proposed in this paper. Firstly, the controllable model of distribution generation (DG), the battery energy storage model with cycle life degradation and the ZIP load model were considered, and the optimization model of AC/DC hybrid distribution network economic operation was constructed. Then the ellipsoid set was used to describe the uncertainty of DG output, and the linear decision-making mechanism of decision variables and uncertainty variables of DG output in the optimization model was formed by combining linear affine strategy, Finally, the robust optimization model of AC/DC hybrid distribution network was transformed into a deterministic mixed integer second-order cone programming model by dual transformation. The effectiveness and advantages of the affinely adjustable robust optimization method proposed in this paper were compared and verified in the two modified IEEE bus system.
Distributed Generations (DGs) with Electric Vehicles (EVs) now play a key role in the planning of power distribution systems. The integration of DGs with EVs planning in the power distribution system is becoming increasingly significant in recent years. This paper presents a review of different optimization techniques for DGs, EVs, and DGs with EVs planning in distribution systems with different load models. The optimization techniques are broadly classified into five categories such as conventional, optimization, artificial intelligence, hybrid, and recent optimization techniques for planning. The different system performances are considered for planning with different goal function viewpoints such as minimization of real and reactive power losses of the system. This review paper also provides an overview of the state-of-the-art models and methods applicable to the DGs with EVs planning, analyzing, and classifying existing and future research trends in this field. Authors strongly believe that this review article would be very useful for researchers, industrial persons, academicians, and scientific persons for finding out the relevant references in the same field.
In this letter, the voltage sensitivity-based coherent reactive power control is proposed to perform the online voltage security enhancement for insecure buses in the multi-area wind power generation systems. The proposed approach is realized in the two-level optimization problem, containing a master problem and two subproblems. The master problem is responsible for the equal reactive power adjustments in multiple regional clustering wind farms regarding to the voltage security enhancement while the subproblems proceed the further improvement of the voltage security based on the analysed result of the master problem. Case studies on IEEE 57-bus test system are presented to validate the cost-effectiveness of the proposed method. Also, the time-domain simulation is conducted to illustrate the validity of the proposed control scheme.
The transactive energy framework was proposed in the recent years to enable the distributed energy trading among distributed integrated energy market. However, sharing energy might lead to profit loss to some participants. As a result, the incentive of joining the coalition for some participants will be diminished. Currently, researches have focused on encouraging participants to share energy by offering compensation, but the incentive compatibility is not modeled effectively. To address this problem, this paper, for the first time, develops an optimization model for the market organizer (i.e., the integrated energy service provider) to allocate the cooperative surplus according to the market contribution while the crucial attributes of the market mechanism are ensured (i.e., social welfare maximization, individual rationality, cost recovery and incentive compatibility), so that market participants can earn their fair shares and profit reductions can be circumvented. On the other hand, the problem caused by the highly uncertain and intermittent power outputs from distributed renewable energy resources imposes additional challenge for organizing a transactive energy market, which calls for the exploitations of effective algorithms to identify representative scenarios. In this paper, adaptive robust optimization algorithm is adopted to identify the scenarios that enhances conservativeness and economy and improves the efficiency of market clearing process, which transforms the problem into a tri-level model and solves by iterating between internal and external formulations. Simulation results show that social welfare increases $2,751 compared with independent operation mode, and calculation efficiency can be improved by 71.55% utilizing the ARO method.
Optimal reactive power management is one of the key operational aspects for efficient planning of distribution system. This study has become more challenging with renewable energy sources integration into the system. Objective of this paper is to address optimal reactive power planning in distribution system using hybrid optimization. This paper presents a comprehensive model for solving capacitor allocation, PV penetration and feeder reconfiguration simultaneously. The objective function considered in this paper includes cost of capacitor, PV and energy loss. The proposed work includes: (i) capacitor allocation using ant lion optimization, (ii) capacitor rating determination using general algebraic modeling system, (iii) reconfiguration of distribution system using genetic algorithm, (iv) investigated different case studies to realize the impact of various load models and load growth factors on reactive power compensation and network reconfiguration, (v) real-time procedure to determine rating of capacitor banks for practical distribution systems, (vi) reactive power dispatch for realistic time-varying load model considering coordination among capacitors, on-load tap changer and photo-voltaic (PV) penetration. The analysis is carried out considering uncertainty of load demand and PV power generation. The proposed methodology is implemented on IEEE 33-bus distribution system and real-time existing Indian distribution network to demonstrate effectiveness of the proposed methodology. It is observed that voltage-dependent load models have a significant impact on system performance and rating of capacitor banks. The proposed strategy ensures optimal solution compared to other existing techniques.
The swift technological progress of inverter-based distributed energy resources (IBDERs) has led to proliferation of such DERs in electric power grids. Despite sequel of advantages, IBDERs integration render serious impacts pertaining to the protection, control, operation, and planning, of power system, particularly at distribution level. Accordingly, this paper aims at exploring the challenges, opportunities, and future trends of IBDER-integrated distribution grids. Via illustrative examples, the new insights on different aspects of protection, control, operation , and planning solutions are drawn and discussed which paves the way for future researches in this area. The struggle is, however, by no means over and extensive future research efforts are to be conducted for leveraging advanced techniques and technologies to address techno-economic concerns.
Aiming at the problem that the reactive power optimization of traditional distribution network can’t adapt to the active distribution network (ADN) of large-scale distributed power access, a comprehensive reactive power optimization method based on quantum krill herd algorithm for ADN is proposed. Firstly, a reactive power optimization model of ADN based on proportional coefficient is proposed by analyzing the characteristics of active control and active management. Secondly, the krill herd algorithm is easy to fall into local optimum when solving optimization problems. To overcome this shortcoming, a quantum krill swarm algorithm is proposed. The algorithm uses the probability amplitude of quantum bits to represent the information of particle position, uses quantum revolving gate to increase population diversity and generates new population through chaotic crossover, which improves the convergence accuracy of the algorithm. Finally, simulation experiments are carried out on the modified IEEE33 node and IEEE 69 node to verify the effectiveness of the proposed model and algorithm.
The swift technological progress of inverter-based distributed energy resources (IBDERs) has led to proliferation of such DERs in electric power grids. Despite sequel of advantages, IBDERs integration render serious impacts pertaining to the protection, control, operation, and planning, of power system, particularly at distribution level. Accordingly, this paper aims at exploring the challenges, opportunities, and future trends of IBDER-integrated distribution grids. Via illustrative examples, the new insights on different aspects of protection, control, operation, and planning solutions are drawn and discussed which paves the way for future researches in this area. The struggle is, however, by no means over and extensive future research efforts are to be conducted for leveraging advanced techniques and technologies to address techno-economic concerns.
The development of intelligent demand-side management with automatic control enables a large amount of residential demands to provide efficient demand-side ancillary services for load serving entities (LSEs). In this paper, we introduce the concept of a comfort indicator, present an advanced reward system, and finally propose a framework for aggregating residential demands enrolled in incentive-based demand response (DR) pro-grams. The proposed framework not only allocates demand reduction requests (DRRs) among residential appliances quickly and efficiently without affecting residents’ comfort levels but also re-wards residential consumers based on their actual participation. Also, since the framework is designed with the practical considerations of simplicity and efficiency, it can be utilized as a quick implementation for existing pilot development works. The effectiveness and merit of this framework are demonstrated and discussed in the comparison studies with conventional incentive-based DR
Due to the development of intelligent energy management system with automatic control, the large population of residential appliances have the opportunity to be effectively utilized by load serving entities (LSEs) to reduce their operating costs and increase total profit. In practice, LSEs are promoting various demand response (DR) programs to stimulate the flexibility of industrial and commercial demand. However, in the residential sector, due to customers' versatile electricity consumption patterns, fully utilizing the responsive residential demand through DR programs such as incentive based demand response (I-DR) is difficult. Specifically, in I-DR, the most crucial issue for LSEs is how to estimate the residents' potential responses to certain financial incentives. Therefore, this paper presents an approach which integrates three data sets (1. the residential energy consumption survey by the U.S. energy information administration; 2. the American time use survey by the U.S. Department of Labor; and 3. the survey of customers' reactions to financial incentives in DR program by the center for ultra-wide-area resilient electric energy transmission networks) to assess responsive residential demand in a stochastic model. This proposed approach can be easily customized for any given times, locations, financial incentives, and residents' portfolios. Also, it will help LSEs get the valuable insights on regulating the residential demand by adjusting the financial incentives to customers and improving the mechanism existing demand response programs.
This paper addresses the problem of voltage regulation in power distribution networks with deep-penetration of distributed energy resources, e.g., renewable-based generation, and storage-capable loads such as plug-in hybrid electric vehicles. We cast the problem as an optimization program, where the objective is to minimize the losses in the network subject to constraints on bus voltage magnitudes, limits on active and reactive power injections, transmission line thermal limits and losses. We provide sufficient conditions under which the optimization problem can be solved via its convex relaxation. Using data from existing networks, we show that these sufficient conditions are expected to be satisfied by most networks. We also provide an efficient distributed algorithm to solve the problem. The algorithm adheres to a communication topology described by a graph that is the same as the graph that describes the electrical network topology. We illustrate the operation of the algorithm, including its robustness against communication link failures, through several case studies involving 5-, 34-, and 123-bus power distribution systems.
In this paper, we propose a robust real-time wind power dispatch framework for coordinating wind farms, automatic generation control (AGC) units, and nonAGC units, which enables wind farms to operate flexibly using maximum power-point tracking strategies. Robust real-time dispatch is formulated as an adjustable robust optimization model incorporating an affinely adjustable controlling strategy compatible with AGC systems. The proposed model can be equivalently transformed to a nonlinear programming problem with linear constraints via duality. The proposed model can also be approximately reduced to a quadratic programming with the objective function simplified. Monte Carlo simulations are carried out to compare the performance of the proposed method against the conventional real-time dispatch scheme. The results show the proposed scheme is robust and reliable.
A centralized reactive power compensation system is proposed for low voltage (LV) distribution networks. It can be connected with any bus which needs reactive power. The current industry practice is to locally install reactive power compensation system to maintain the local bus voltage and power factor. By centralizing capacitor banks together, it can help to maintain bus voltages and power factors as well as reduce the power cable losses. Besides, the centralized reactive power system can be easily expanded to meet any future load increase. A reasonably sized centralized reactive power compensation system will be capable of meeting the requirements of the network and the optimization algorithm proposed in this paper can help to find this optimal size by minimizing the expected total cost ( ETCH). Different load situations and their respective probabilities are also considered in the proposed algorithm. The concept of the centralized reactive power compensation system is applied to a local shipyard power system to verify its effectiveness. The results show that an optimally sized centralized reactive power system exists and is capable of maintaining bus voltages as well as reducing the power losses in the distribution network. A significant power loss reduction can be obtained at the optimal capacity of the centralized reactive power compensation system in the case study.
This paper proposes a fully distributed reactive power optimization algorithm
that can obtain the global optimum of non-convex problems for distribution
networks without a central coordinator. Second-order cone (SOC) relaxation is
used to achieve exact convexification. A fully distributed algorithm is then
formulated corresponding to the given division of areas based on an alternating
direction method of multipliers (ADMM) algorithm, which is greatly simplified
by exploiting the structure of active distribution networks (ADNs). The problem
is solved for each area with very little interchange of boundary information
between neighboring areas. The standard ADMM algorithm is extended using a
varying penalty parameter to improve convergence. The validity of the method is
demonstrated via numerical simulations on an IEEE 33-node distribution network,
a PG&E 69-node distribution system, and an extended 137-node system.
The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.
The increasing penetration of distributed generation
(DG) power plants into distribution networks (DNs) causes var-
ious issues concerning, e.g., stability, protection equipment, and
voltage regulation. Thus, the necessity to develop proper control
techniques to allow power delivery to customers in compliance with
power quality and reliability standards (PQR) has become a rele-
vant issue in recent years. This paper proposes an optimized dis-
tributed control approach based on DN sensitivity analysis and
on decentralized reactive/active power regulation capable of main-
taining voltage levels within regulatory limits and to offer ancillary
services to the DN, such as voltage regulation. At the same time, it tries to minimize DN active power losses and the reactive power
exchanged with the DN by the DG units. The validation of the proposed control technique has been conducted through a several number of simulations on a real MV Italian distribution system.
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject.Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution.The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations.An essential book for anyone working on optimization and decision making under uncertainty,Robust Optimizationalso makes an ideal graduate textbook on the subject.
Optimal power flow (OPF) is considered for microgrids, with the objective of
minimizing either the power distribution losses, or, the cost of power drawn
from the substation and supplied by distributed generation (DG) units, while
effecting voltage regulation. The microgrid is unbalanced, due to unequal loads
in each phase and non-equilateral conductor spacings on the distribution lines.
Similar to OPF formulations for balanced systems, the considered OPF problem is
nonconvex. Nevertheless, a semidefinite programming (SDP) relaxation technique
is advocated to obtain a convex problem solvable in polynomial-time complexity.
Enticingly, numerical tests demonstrate the ability of the proposed method to
attain the globally optimal solution of the original nonconvex OPF. To ensure
scalability with respect to the number of nodes, robustness to isolated
communication outages, and data privacy and integrity, the proposed SDP is
solved in a distributed fashion by resorting to the alternating direction
method of multipliers. The resulting algorithm entails iterative
message-passing among groups of consumers and guarantees faster convergence
compared to competing alternatives
J. F. Benders devised a clever approach for exploiting the structure of mathematical programming problems withcomplicating variables (variables which, when temporarily fixed, render the remaining optimization problem considerably more tractable). For the class of problems specifically considered by Benders, fixing the values of the complicating variables reduces the given problem to an ordinary linear program, parameterized, of course, by the value of the complicating variables vector. The algorithm he proposed for finding the optimal value of this vector employs a cutting-plane approach for building up adequate representations of (i) the extremal value of the linear program as a function of the parameterizing vector and (ii) the set of values of the parameterizing vector for which the linear program is feasible. Linear programming duality theory was employed to derive the natural families ofcuts characterizing these representations, and the parameterized linear program itself is used to generate what are usuallydeepest cuts for building up the representations.In this paper, Benders'' approach is generalized to a broader class of programs in which the parametrized subproblem need no longer be a linear program. Nonlinear convex duality theory is employed to derive the natural families of cuts corresponding to those in Benders'' case. The conditions under which such a generalization is possible and appropriate are examined in detail. An illustrative specialization is made to the variable factor programming problem introduced by R. Wilson, where it offers an especially attractive approach. Preliminary computational experience is given.
In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering. Comment: 50 pages
The United Kingdom is leading Europe in its power market reforms and is currently engaged in addressing issues of DG integration through development of innovative approaches to network planning, operation, regulation, and pricing. This article draws on these recent advances to explore active management technologies, developing regulatory arrangements for network operation, and new commercial opportunities emerging from the low-carbon climate change agenda. It explores the changing role of the distribution system operator (DSO) in response to increasing penetration of DG and identifies the transitions currently taking place in the United Kingdom toward integration of DG that has wider implications for extrapolation across Europe.
Using energy from renewable sources is supported by the idea of environmental conservation and because of insufficient fossil fuels. Recent studies suggest that in the medium and long term, photovoltaic generators (PV / FV) will become so attractive from the commercial standpoint, that their implementation will be seen in many parts of the world. A current challenge in the energy field is to determine how variable (renewable - solar / wind) production plants should contribute to the proper operation of the power distribution network. In the first phase, the work deals with strategies needed to control the reactive power within photovoltaic parks. The second part of the paper deals with the ability to adjust the National Energy System (SEN) through inductive reactive / capacitive power compensation. The last part presents the conclusions and the obtained results.
Active power dispatch and reactive power optimization problems are usually handled separately in active distribution systems, aiming at minimizing the total generation cost or transmission losses. However, the separate optimization cannot achieve a global optimum scheme in distribution system operations. Moreover, the significant relationship between the active power and reactive power may pose great challenges to distribution system operations due to the uncertain nature of load demands and intermittent renewable energy resources. In this paper, using the branch flow model based relaxed optimal power flow (BFM-ROPF), we formulate a robust coordinated optimization problem for active and reactive powers as a mixed integer second order cone (SOC) programming problem. Furthermore, in order to address the uncertainties, a two-stage robust optimization model is proposed to coordinate the on load tap changer ratios, reactive power compensators, and chargedischarge power of energy storage system (ESS) to find a robust optimal solution. Then the column-and-constraint generation algorithm is applied to solve the proposed robust two-stage optimization model. In the ROPF, a stricter cut is added to speed up the computation process of the SOC relaxation in order to guarantee the exactness for representative cases, such as those with high penetration of distributed energy resources (DERs). Numerical results based on the 33-bus and 69-bus systems verify the effectiveness of the proposed method.
This study proposes a coordinated and distributed smart grid based method for optimal set points of distributed generators (DGs) as well as on-load tap changer (OLTC) transformer in the unbalanced power distribution grid. In this approach, energy efficiency is improved and voltage profile is kept in an acceptable range under different operating conditions of renewable energy resources and loads. In the first level of proposed hierarchical method, DGs are independently trying to keep voltage profile in an acceptable range in an optimal way. If the first level is not succeeding, at the second level, OLTC cooperates with the first level to eliminate the voltage violation in the system. The rules for updating control setting are derived based on distributed gradient algorithm. The proposed distributed solution presents analogous steady state results in comparison with centralised optimisation solution. Since the processing data is reduced, short-timed response to change operating conditions could be achieved. Simulation results on three distribution test systems with different sizes demonstrate the effectiveness of proposed control solution.
Optimally dispatching Photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most significant impact on the network voltage level. Following, it ensures the optimal set-points of both active power and reactive power for the selected inverters, guaranteeing the entire system operating constraints (e.g., the network voltage magnitude) within reasonable ranges. However, the intermittent nature of solar PV energy may affect the selection of the critical PV inverters and also the final optimal objective value. In order to address this issue, a two-stage robust optimization model is proposed in this paper to achieve a robust optimal solution to the PV inverter dispatch, which can hedge against any possible realization within the uncertain PV outputs. In addition, the conic relaxation-based branch flow formulation and second-order cone programming based column-and-constraint generation algorithm are employed to deal with the proposed robust optimization model. Case studies on a 33-bus distribution network and comparisons with the deterministic optimization approach have demonstrated the effectiveness of the proposed method.
Due to the increasing penetration level of renewable energy resources in distribution systems, a new approach for voltage and reactive power control needs to be developed to deal with the fluctuations caused by intermittent changes of these resources. This paper, at first proposes a systematic approach which splits distribution networks into several separate control areas to control the voltage profile of each area in an admissible time calculation which is necessary for online operation. Then to solve the voltage regulation problem, a new method based on particle swarm optimization (PSO) algorithm is proposed to be applied to each center using only zonal data and also to calculate the optimum reactive power set points of the distributed generations (DG) units in the related area. Finally, the results of the proposed method on IEEE 123-bus unbalanced test system in comparison with another method based on interior point algorithm in both decentralized and centralized manner demonstrate the ability and efficiency of the proposed method for online control of smart grids voltage profile.
Prompted by technical issues that have arisen due to the widespread deployment of distributed intermittent renewable generators, rapidly rising peak demand and reductions in battery price, the use of battery-based energy storage systems in power networks is on the rise. While battery-based energy storage has the potential to deliver technical benefits, the best possible sizing, location and usage govern the financial viability. The objective of this study is twofold. Firstly, a generalised approach is proposed to model network upgrade deferral as a function of load growth rate, renewable generation penetration and peak shave fraction. This model is then used for the formulation of an optimisation problem which benefits from multi-period power flow analysis to co-optimise battery size, location, charge/discharge profile for a pre-specified number of units to be deployed in a given distribution network. The proposed approach is implemented using the generic algebraic modelling system platform and validated on an Australian medium voltage distribution network under multiple practical and potential future scenarios.
The paper presents a network partitioning strategy for the optimal voltage control of Active Distribution Networks (ADNs) actuated by means of a limited number of Distributed Energy Storage Systems (DESSs). The proposed partitioning uses a linear programming approach by means of the known concept of voltage sensitivities. Then, two decentralized optimal control algorithms are proposed relying, respectively, on the Thévenin equivalents and a recursive approach. These algorithms are developed using the Multi-Agent System (MAS) concept. With respect to a centralized control algorithm, the aim of the network clustering is to reduce the number of exchanged messages among the clusters when one of the two proposed decentralized control algorithms is adopted. The effectiveness of the two proposed controls is assessed with respect to the performances of the equivalent centralized control using numerical examples composed by the IEEE 13 and IEEE 123 buses distribution test feeders adapted to include stochastic generation and DESSs.
The responses of multiple distributed generation (DG) units and voltage regulating devices, such as tap changers and capacitor banks, for correcting the voltage may lead to operational conflicts and oscillatory transients, where distribution systems are subjected to network reconfiguration and availability of the DG units. Therefore, coordinated voltage control is required to minimize control interactions, while accounting for the impact of structural changes associated with the network. This paper proposes a strategy for coordinating the operation of multiple voltage regulating devices and DG units in medium voltage (MV) distribution systems, under structural changes and DG availability, for effective voltage control. The proposed strategy aids to minimize the operational conflicts by allowing voltage regulating devices to operate in accordance with the priority scheme designed based on the electrical-distance between voltage regulating devices and DG units, while maximizing the voltage support by the DG units. The proposed coordination scheme is designed to enact with an aid of a substation centered distribution management system for online voltage control. The control actions of proposed coordination strategy are tested on a MV distribution system, derived from the state of New South Wales, Australia, through simulations, and results are reported.
The increasing penetration of fluctuating photovoltaic (PV) generation brings operational challenges for distribution system operators, such as introducing the voltage rise problem. The situation is made worse in the presence of single-phase generation being unevenly connected to the different phases. To address this problem, distribution transformers with single-phase tapping capability, together with reactive power provision of PV systems, are under investigation. This paper presents modeling and analysis of the benefits of coordinated voltage control of a decoupled three-phase on-load tap changer (OLTC) and photovoltaic inverters in a distribution system, for accommodating a greater number of photovoltaic generators in the grid. A 24 h root-mean-square simulation study is performed in the DigSilent PowerFactory with a 1 s time step using 10 min resolution consumption and production profiles on a real Danish distribution grid, as well as the developed dynamic photovoltaic generation and load models. The simulations show that the joint action of the power distribution transformer with OLTC control on each phase, and the reactive power provision of photovoltaic inverters, significantly improves the PV hosting capacity in the analyzed unbalanced scenarios without side effects, such as additional power losses, or significant neutral voltage rises.
This paper proposes a decentralized controller to coordinate the reactive power injections of PV generators in order to contribute to the voltage regulation in distribution networks. The control actions are evaluated in the real time by adopting an optimization methodology involving the sensitivity applied to the Lyapunov function. By this approach it is possible to derive an auto-adaptive algorithm that can be implemented on actual distribution network without implying additional costs for infrastructures. Computer simulations have been performed on a MV distribution system to demonstrate the effectiveness of the proposed control scheme at different operating conditions and to confirm its ability to work in the real time.
With sustained growth of intermittent power supplies connected to grid, the randomness and volatility of intermittent power supplies will bring new challenges to power system optimization dispatch. For dealing with the uncertainty of large-scale intermittent power supply, this paper introduces robust optimization theory into optimization dispatch for hybrid wind/photovoltaic/hydro/thermal power systems. Meanwhile in order to make an ideal compromise between reliability and economy of system dispatch, this paper also introduces concept of uncertainty to cover shortcoming of conventional robust optimization which is conservative. A flexible robust optimization with adjustable uncertainty budget dispatch model is built for hybrid power system to achieve coordination between reliability and economy, and then the uncertainty budget decision methods are studied to reduce blindness of uncertainty budget decisions. Moreover, a new compound differential evolution (CDE) algorithm is designed in this paper. The diversity of individuals and convergence speed are taken into account in process of differential evolution (DE) calculation by introducing a series of operations into DE algorithm, such as individual ranking, population dividing, CDE, and population restructuring. Finally, a sample application is carried out to verify the validity and practicability of the model and the method.
In this paper, we propose a method to optimally set the reactive power contributions of distributed energy resources (DERs) present in distribution systems with the goal of regulating bus voltages. For the case when the network is balanced, we use the branch power flow modeling approach for radial power systems to formulate an optimal power flow (OPF) problem. Then, we leverage properties of the system operating conditions to relax certain nonlinear terms of this OPF, which results in a convex quadratic program (QP). To efficiently solve this QP, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM). Furthermore, we include the unbalanced three-phase formulation to extend the ideas introduced for the balanced network case. We present several case studies to demonstrate the method in unbalanced three-phase distribution systems.
Time-varying renewable energy generation can result in serious
under-/over-voltage conditions in future distribution grids. Augmenting
conventional utility-owned voltage regulating equipment with the reactive power
capabilities of distributed generation units is a viable solution. Local
control options attaining global voltage regulation optimality at fast
convergence rates is the goal here. In this context, novel reactive power
control rules are analyzed under a unifying linearized grid model. For
single-phase grids, our proximal gradient scheme has computational complexity
comparable to that of the rule suggested by the IEEE 1547.8 standard, but it
enjoys well-characterized convergence guarantees. Adding memory to the scheme
results in accelerated convergence. For three-phase grids, it is shown that
reactive injections have a counter-intuitive effect on bus voltage magnitudes
across phases. Nevertheless, when our control scheme is applied to unbalanced
conditions, it is shown to reach an equilibrium point. Yet this point may not
correspond to the minimizer of a voltage regulation problem. Numerical tests
using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison
47-bus feeder with increased renewable penetration verify the convergence
properties of the schemes and their resiliency to grid topology
reconfigurations.
Increasing penetration of photovoltaic (PV), as well as increasing peak load demand, has resulted in poor voltage profile for some residential distribution networks. This paper proposes coordinated use of PV and battery energy storage (BES) to address voltage rise and/or dip problems. The reactive capability of PV inverter combined with droop-based BES system is evaluated for rural and urban scenarios (having different ratios). Results show that reactive compensation from PV inverters alone is sufficient to maintain acceptable voltage profile in an urban scenario (low-resistance feeder), whereas coordinated PV and BES support is required for the rural scenario (high-resistance feeder). Constant, as well as variable, droop-based BES schemes are analyzed. The required BES sizing and associated cost to maintain the acceptable voltage profile under both schemes are presented. Uncertainties in PV generation and load are considered, with probabilistic estimation of PV generation and randomness in load modeled to characterize the effective utilization of BES. Actual PV generation data and distribution system network data are used to verify the efficacy of the proposed method.
This paper deals with the development of a centralized nonlinear auto-adaptive controller able to optimize the network voltage profile by managing the reactive power supplied by PV inverters. The control design is based on a realtime optimization procedure involving the sensitivity theory in conjunction with the Lyapunov function and produces the control laws that must be sent to local controllers of PV-inverters. The derived controller is implemented and tested on a MV distribution network.