Load Curtailment Research Articles

Operating Reliability Evaluation of Power Systems With Demand-Side Resources Considering Cyber Malfunctions

Demand-side resources (DSRs) have been shown to be valuable for providing reserve capacity and enhancing the reliability of power systems with high wind power penetration. The successful utilization of DSRs relies heavily upon the infrastructure of advanced information and communication technologies (ICTs). Notably, ICT systems may suffer from cyber attacks and communication latency, which could result in the malfunctions of DSRs and consequently bring adverse impacts on the reliability of power systems. In this paper, a novel operating reliability evaluation framework for multi-state power systems with DSRs and wind power considering malfunctions of cyber systems is proposed. For avoiding increasing complexity caused by multiple system states, an analytic method based on Lz transform technique is proposed to achieve dynamic system reliability. The reliability model for a typical hierarchical decentralized control infrastructure in demand side considering cyber attacks and communication latency is first proposed. Then, reserve capacity from DSRs considering stochastic behavior under the cyber infrastructure is modelled. Moreover, multi-state models for power generation systems with stochastic wind power and conventional generation are developed considering cyber malfunctions. Reliability indices based on load curtailment by conducting optimal power flow for various system states are utilized for reliability assessment. A modified IEEE RTS with four cases is adopted to validate the proposed model and method, which denotes that reserve capacity from DSRs can definitely enhance system operating reliability and affected by proportions of DSRs, consideration of cyber malfunctions, actual committed time for DSRs and initial system conditions.

Optimal Demand Response Management of a Residential Microgrid Using Model Predictive Control

Demand response (DR) is an important factor contributing to achieve a balance between energy production and demand in Smart Grids. DR plays a key role in the use of residential energy allowing to improve the load management, electrical grid reliability, to reduce energy demand during peak hours and to minimize the use of energy in face of increasing energy prices. This article proposes a Model Predictive Control (MPC) strategy to manage the energy resources of a residential microgrid combined with DR techniques, such as load curtailment, that promotes short term reduction of electricity demand in pre-defined hours. In particular, the presented approach encompasses degradation issues of the Energy Storage System (ESS), the cost of the electricity, renewable energy generation, and other operational constraints. The developed control strategy is able to maximize microgrid economical benefit, while minimizing the degradation of the ESS, reducing electricity consumption during the day, and fulfilling the different operational constraints. The proposed strategy is validated in an experimental renewable-energy based microgrid platform for different climatic conditions. The obtained results demonstrate and verify the effectiveness of the proposed control and management strategy.

Activating current and future flexibility potential in the distribution grid through local energy markets

Increasing shares of volatile generation and additional peak demand through electric vehicles and heat–pumps challenge the stable operation of current and especially future distribution grids. In order to cope with these challenges, several approaches to reduce peak feed-in and demand are possible. For example, the curtailment of renewable generation or peak loads or the activation of demand-side flexibility through, e.g., time of use tariffs. Local energy markets (LEMs) are a promising alternative concept to combine various of these measures and activate untapped flexibility potential of the demand- and generation-side as well as storages. In this study, the authors propose a linear optimisation-based market mechanism that can explicitly consider demand, generation and storage flexibility in the form of flexibility orders. Through the simulation of a LEM in a rural region of Germany with a high share of renewable feed-in, they demonstrate the effects of the proposed LEM for the current situation and a future scenario with additional load through electric vehicles and heat pumps. The results show that, compared to a business as usual case, the LEM can provide both peak-shaving of PV feed-in during midday and a significant reduction of evening demand peaks of up to 23%.

Resilience Improvement With Zero Load Curtailment by Multi-Microgrid Based on System of Systems

This article studies the resilience and energy management in multi-microgrid system. In the proposed model, the microgrid is formed by four sub-microgrids. Based on the system of systems (SOS), these sub-microgrids pool their resources and capacities together to form a new and more complex microgrid that provides further functionality than the basic separated microgrids. One of the sub-microgrids is connected to the external grid. The tie-line connections are between all sub-microgrids. Some connections are normally-open and the rest are normally-closed. The status of normally-open connections is changed to close when the resilience or economic criteria dictate. The sub-microgrids are integrated with solar panels, wind turbines, battery energy storage (BES) and loads. All sub-microgrids are also equipped with diesel generator as emergency resource. Under normal operating condition, the proposed model optimally utilizes the resources of all sub-microgrids to minimize the cost, pools the extra resources of sub-microgrids, and optimizes the operation of batteries and diesel generators. Under faulty operating condition when some or all sub-microgrids are islanded, the model supplies the loads with zero load curtailment and minimizes the costs. In the faulty condition, the model may change the status of connections from normally-open to close when required. The simulation results on a given test system verify that the recommended model confirms optimal operation of the microgrid. Furthermore, all sub-microgrids, improves resilience, minimizes operating cost, handles the events and achieves zero load curtailment under both faulty and healthy conditions.

Flexible Coordination Optimization Scheduling of Active Distribution Network With Smart Load

The multiple uncertainties caused by high penetration renewable energy sources (RESs) and loads access place demanding requirements on the flexibility of distribution networks. To optimize the flexible sources, a multi-time scale flexible dynamic optimization model is proposed based on active distribution network (ADN) with smart loads participation. The long-time-scale stage aims to reduce the comprehensive cost and smooth the net load fluctuations. In this stage, two indicators, namely, scheduling cost and remaining scheduling capacity ratio are designed to update the output order of dynamic regulation equipment (RESs, energy storage systems). Meanwhile, interruptible load curtailments are also coordinated in the power generation plan. Using the net load fluctuations constraint, the flexibility margin for ADN is more sufficient. In the short-term-scale stage, smart loads are introduced to utilize the load-side demand response potential and expand the margin of flexibility. For consumers, a win-win cooperation between ADN and users can be achieved with utility model, which guaranteed the reliable regulation capacity to support better power quality. And for ADN, the economic costs are further reduced, along with the elimination of the voltage violation. Case studies are conducted to demonstrate the performance of the proposed strategy.

Impact of Down Spinning Reserve on Operation Reliability of Power Systems

The development of renewable energy and the increasing peak-valley difference of load demand lead to an increasing requirement of spinning reserve (SR). However, the traditional operation reliability analysis of power system mainly focuses on the up SR and neglects the down SR. Therefore, the operation reliability of power system considering the impacts of down SR is investigated in this paper. Firstly, the constraints of down SR are incorporated into the day-ahead unit commitment (UC) model to obtain the generation scheduling and reserve allocation of power systems. Based on the dispatch results of UC model, the re-dispatched energy and load interruption can be determined using optimal power flow (OPF) model in the real-time operation in various contingency states. Operation reliability indices are calculated based on the load curtailment to represent the reliability performances of power systems. The proposed approaches are validated using the modified IEEE reliability test system. Case studies demonstrate that down SR can improve the operation reliability of power systems.

Dynamic Optimal Power Flow Incorporating Gas Turbine Generator Unit with Compressed Natural Gas Systems

In developing countries, gas infrastructures are not well established yet. Consequently, insufficient gas supply for gas turbine generator unit may occur during peak load hours. Dual firing unit can overcome such limitation by switching the fuel to High Speed Diesel Oil. However, it will significantly increase the operation cost since High Speed Diesel fuel is much more expensive than natural gas. Recently, a compressed natural gas system has been widely used to solve inadequacy of gas supply during peak load hours. This system compresses the remaining natural gas supply from gas provider during off-peak load hours and stores it into gas tubes. The compressed gas is then released to top up gas supply to gas turbine during peak load hours. Thus, the unit can fully operate on gas at both peak and off-peak load hours. As a result, the operation cost will be lower. This paper proposes an approach in optimizing system operation cost of large system, which has gas turbine generator unit with compressed natural gas system. Furthermore, load curtailment and implementation of take or pay energy contract is also included. The problem is formulated as dynamic optimal power flow and solved using quadratic programming. The proposed approach is tested using IEEE 30 bus and Jawa Bali 500 kV. The results show the effectiveness of the approach in optimizing daily gas usage as well as satisfying take or pay energy contract.

Reliability evaluation of bulk power systems using the uniform design technique

Reliability evaluation of bulk power systems (BPSs) has inherent computational complexity due to the numerous system states and the time-consuming system state analysis, including power flow calculation, load curtailment, recognition of split power systems and network reconfiguration. In this study, a novel uniform-design based method is proposed to improve the computational efficiency of power system reliability evaluation. The main idea is that the uniform-design technique is used to generate the system states in reliability evaluation, which makes the sampled states more uniform and representative in the overall state space compared to the enumerated system states in an analytical method or the random-generated system states in Monte Carlo simulation. As a result, the sample size and the computational time can be significantly reduced. In addition, the confidence intervals of the reliability indices, such as LOLP, FLOL and EENS, are given. And the estimation errors of reliability indices are discussed. The proposed method is tested on several BPSs, including the IEEE-RTS79, IEEE-RTS96 and a real BPS in China. All the case studies indicate that the proposed technique can significantly improve the computational efficiency of BPS reliability evaluation.

Comfort evaluation of seasonally and daily used residential load in smart buildings for hottest areas via predictive mean vote method

In this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mamdani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonally used appliances are considered for the analysis of the efficient energy management through these controllers. Energy management is performed using the two Demand Side Management (DSM) strategies: load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of the daily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances, i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are applied in this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of the hottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occupancy, price, decision control variables, priority and length of operation times of the appliances, whereas the output parameters are: energy consumption, cost and thermal and appliance usage comfort. Moreover, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger's predictive mean vote method. The comfort is further investigated by incorporating the renewable energy sources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllers as compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energy consumption and cost of the proposed scenario.

Renewable energies and storage in small insular systems: Potential, perspectives and a case study

Insular electricity systems are of importance, as the security of supply, the competitiveness and the sustainability of the islands’ populations depend on non- or weakly interconnected electrical systems. For this reason, they have until now been treated with a good degree of conservatism in relation to both the power generation technologies used and the regulatory framework applied. This, however, results in power generation costs significantly higher than those of the interconnected systems, whilst it is also linked with high local environmental burdens.However, developments in the fields of renewable energy systems technologies, of weather prediction models and of grids and micro-grids management allow today for a much more responsive and adaptive management of the insular systems than was possible only ten years ago. This is further enhanced by the progress made in electrical energy storage, which makes the autonomy of islands a realistic goal, and also a feasible one, especially for small and very small islands.It is against this background that the regulatory framework has also to be re-considered, in order to enable a fair valuation and charging of storage, of load curtailment and eventually of ensured supply and quality of electrical energy. All this needs to be part of the new modus operandi towards decarbonized and sustainable insular communities.

EPEC approach for finding optimal day-ahead bidding strategy equilibria of multi-microgrids in active distribution networks

In this paper, an equilibrium model is proposed to model the operation of a distribution network (DN) consisting of distributed energy resources (DERs) and multi-microgrids (MMGs) with storage devices. The aim is to find equilibria among microgrids (MGs) which are bidding/offering strategically to buy/sell power from/to the DN in a day-ahead market. For finding such equilibria, a bilevel model is proposed in which the bidding problems of the MGs are defined at the upper level, constrained at the lower level by the distribution network operator (DNO) objective function subject to technical constraints. This bilevel model of each MG is transformed into a mathematical program with equilibrium constraints (MPEC). The aggregated solutions of all MPECs form an equilibrium problem with equilibrium constraints (EPEC). The solutions of this EPEC are analyzed through a diagonalization method to identify meaningful Nash equilibria. Different case studies are carried out to find equilibrium in an illustrative test case based on the IEEE 33-node network. An extensive battery of numerical simulations suggests that the progressive installation of storage devices and the possibility of load curtailment by MG have a strong impact on the market equilibrium and on the operation costs of the DN.

Nodal Reliability Evaluation for a VSC-MTDC-Based Hybrid AC/DC Power System

The utilization of voltage-source converter-based multi-terminal HVDC (VSC-MTDC) technology has been envisaged as a cost-effective solution to transmit bulk renewables from multiple sending ends to receiving-end AC systems. However, with complex configurations and various types of components, the reliability evaluation of a VSC-MTDC system is more complicated than that of a conventional HVDC system. In addition, due to stochastic failures and complex operating modes, the application of a VSC-MTDC system can have a significant influence on the reliability of hybrid AC/DC power systems. This paper presents a new technique for evaluating the nodal reliability of a hybrid AC/DC power system based on the VSC-MTDC system. The proposed technique is incorporated within a multi-state model for the VSC-MTDC system. Then, an optimization model for contingency management of a hybrid AC/DC power system is formulated. The model is established based on the optimal power flow technique for a VSC-MTDC based hybrid AC/DC power system. The objective of the model is to minimize the total system cost when determining the load curtailment and generation re-dispatch results for each contingency state. Nodal reliability indices for hybrid AC/DC power systems are proposed to evaluate the reliability performance of customers at each node. The effectiveness of the proposed methods is validated using a modified IEEE reliability test system.

A Multi-Objective Planning Framework for Coordinated Generation From Offshore Wind Farm and Battery Energy Storage System

This paper proposes a novel multi- objective planning framework to determine optimal capacity of battery energy storage system (BESS) for coordinated operation of large scale offshore wind farm (OWF) and BESS. Multiple objectives such as battery cost and lifetime, availability of wind turbines (WTs), expected energy not supplied (EENS), loss of load hour (LOLH) and curtailment of wind energy have been considered for determining optimal capacity of BESS for OWF. Wake effect loss in OWF and uncertainty of wind speed have been also incorporated the optimal BESS framework for determining aforementioned objectives. Moreover, as WTs are susceptible to random failure, availability of WTs is an important issue which has been also considered in the planning stage for determining optimal size of BESS in this study. One of the main features of the proposed approach is that it provides flexibility to decision makers in selecting best available alternative for BESS (among different types) to minimize mismatch between forecasted generation schedule and actual generation of OWF, while taking into account economical and operational reliability issues. The effectivene ss of the proposed planning strategy has been validated through different case studies conducted on Horns Rev- 1 OWF situated in Denmark.

DER Aggregator’s Data-Driven Bidding Strategy Using the Information Gap Decision Theory in a Non-Cooperative Electricity Market

When multiple distributed energy resource (DER) aggregators exist in a non-cooperative power market, the calculation of individual aggregator’s bidding strategies could encounter significant uncertainties for considering DERs and competing market participants’ bidding strategies. In this paper, a bi-level bidding strategy optimization model is proposed for a DER aggregator which utilizes wind power, energy storage system (ESS), and curtailable load. At the upper level, the designated aggregator’s bidding strategy is optimized considering the wind power uncertainty. The wind forecast error is modeled by an ambiguity set using the data-driven approach. The information gap decision theory (IGDT) method is employed in this paper to maximize the risk level the designated aggregator can bear for a certain level of expected payoff. By detecting the worst case in wind power generation, the upper-level model is linearized as an MILP. The designated aggregator submits its bids to the market using the linear utility function acquired from linear regression. At the lower level, the market clearing is carried out using competing market participants’ bidding strategy scenarios. The scenarios and the corresponding probability are modeled through a data-driven approach. The market clearing problem is linearized using Taylor series. The price signal is iterated between the two levels as the proposed bi-level model is solved. Numerical results prove the validity and effectiveness of the proposed IGDT-based method. It is shown that the aggregator can adjust either the bidding quantities or coefficients to reach an expected payoff level. The bidding strategies are affected by uncertainties of wind power and competing bidding strategies. For an expected payoff level, when the designated aggregator is posed to consider a higher risk of wind power uncertainty, the aggregator can only bear a lower risk level from competing bidding strategies and vice versa.

Impact Optimal DG Placement Against Harmonic Distribution on Reconfiguration Distribution Network on Microgrid System

Increases in harmonic distortion in Radial Distribution System (RDS) will result in additional shorter insulation lifetime, higher temperature and also malfunction. These are undesirable as they cause power losses and also affect the voltage profile. In this paper the harmonic analysis is carried effect and performance placement Distributed Generation (DG) after reconfiguration distribution network in active radial distribution system or microgrid concept. The optimal size of DG is determining using K-Means Clustering method and Load Curtailment method to designing distribution network reconfiguration after restoration. The load flow analysis and harmonic flow analysis is carried out with direct approach using BIBC and BCBV matrices. The proposed method is carried out on IEEE 33 bus system having the harmonic sources injected as current sources using MATLAB and ETAP software. The result show DG placement on 4 bus points using K-Means Clustering can improve voltage levels and reduce harmonic dispersion. After restoration and reconfiguration of the system, the harmonic spread is strongly influenced by the current flowing on the bus and the location of the source harmonics on the system.

Radial distribution network planning under uncertainty by applying different reliability cost models

The combined steepest decent and artificial annealing optimization method for optimal planning of radial distribution networks is upgraded to take into account the uncertainty of inputs and various methods of determining the load curtailment costs. The optimization method considers the uncertainties of predicted power consumption, of line failure rates and of the power supplied by distributed generators. The search for the optimal network configuration is conducted with various cost models for the curtailed loads, for comparison. The calculations performed have shown that the reliability cost models considerably affect the optimal network configurations and associated total annual costs. Also, it is indicated that the distributed generators should be modeled in the planning of networks as they favorably affect the total costs by decreasing energy losses and by helping in maintaining network operation within the allowed current and voltage limits.

A decentralized load control architecture for smart energy consumption in small islands

In this paper we propose the adoption of Overgrid, a new decentralized load control architecture, for balancing the energy production variations introduced with the adoption of renewable sources, facilitating and improving the smart energy retrofit. The system is presented and applied for managing the aggregated daily load profile of a community of domestic end-users in the island of Lampedusa, Italy, exploiting the load profiles gathered in a real measurement campaign. The Overgrid Demand Response (DR) architecture is used for managing the residential flexible loads, estimating the aggregated power demand without any centralized server and creating a virtual “community” of smart buildings in the small island. Two different control schemes are applied considering load curtailing and load shifting in presence of significant variations of the electricity production in the island. Our simulation experiments show how the proposed architecture, without any centralized control, is able to contrast such events and, thanks to load shifting actions, guarantees also a minimum discomfort for the end-users, opening the path to substantial smart energy retrofit of small island communities.

Study on Emergency Load Shedding of Hybrid AC/DC Receiving-End Power Grid with Stochastic, Static Characteristics-Dependent Load Model

When large-capacity HVDC (high voltage direct current) transmission line blocking occurs in a hybrid AC/DC (alternating current/direct current) power grid, the receiving-end system will encounter a huge power imbalance, which will lead to a frequency drop and redistribution of the power flow, and which may further lead to the overload of other transmission lines, cascading failures and a large-scale blackout. To resolve these problems, an emergency load-shedding strategy for the DC receiving-end system is proposed from the perspective of a quasi-steady state. The proposed method can accurately calculate the actual total power imbalance by modeling more detailed stochastic loads with static frequency/voltage characteristics and involving the inertia effect of the generator during the response delay period, which can effectively reduce the amount of load curtailment. In addition, several factors affecting the power imbalance estimation in stochastic scenarios and their mechanisms are analyzed in detail, and the key aspects relevant to the DC blocking fault analysis are identified as well. Finally, the influence of different load-shedding strategies on the receiving-end system security after a DC blocking fault is compared with the security indices, including those that are relevant to the frequency/load change proposed herein, and a uniform load-shedding coefficient is obtained via the proposed method, even for different power imbalances under a stochastic context, which makes the load-shedding strategy more practical.

How much load flexibility can a euro buy? Findings from a contingent valuation experiment with companies in the German commerce and services sector

Demand-side load management is considered a cost-efficient solution for accommodating growing shares of intermittent renewable electricity production. We employ a double-bounded dichotomous choice contingent valuation (CV) experiment with 275 companies in the German commerce and services sector to estimate the effectiveness of a subsidy to make their ventilation, air conditioning, refrigeration and freezing systems available for automated load management. Our estimates suggest that a given subsidy would unlock more flexible load at lower per MWh subsidy costs from air conditioning compared to ventilation. We find no effect of subsectors and the proposed frequency and duration of the load curtailments on the subsidy level. Subsidy levels in the center of the distribution yield specific subsidy costs that suggest that load management in the commerce and services sector may become a competitive option on the balancing market.

Optimal Resource Management of Microgrids in a Competitive Market Environment Using Dual Layer Control Approach

Renewable integrated microgrids effectively contribute in reducing GHG emissions substantially, at a global level. A multi-agent control system to facilitate information exchange for a microgrid participating in the deregulated framework of the electricity market is proposed. A novel energy management system, aimed at the effective utilization of RES and stored energy in PHESS in order to ensure maximum priority based social benefit to the microgrid controller is presented. The intermittent nature of RES which might cause uncertainties in availability during real-time dispatch, is effectively dealt in the proposed dual layer control approach, through optimal usage of PHESS and employment of load prioritization technique in the proposed renewable microgrid. Thus, the research carried out in this work not only ensures the committed power exchange is maintained constant in both DASL and RTDL, but also contributes significantly in PBSB maximization of the microgrid, while managing real-time uncertainties in RES availability. The importance of pre-prioritizing the loads is put forth, in order to ensure that, if load curtailing needs to be done during peak demand intervals, the critical loads must not suffer. The developed algorithm has been successfully implemented on a 16 bus microgrid interconnected with a 30 bus main grid.