Unit Dispatch Research Articles

Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment

This paper presents a two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the competitive interactions between the electrical generation units. The first stage is focused on the day-ahead scheduling of the thermal power plants while the balancing market dispatch is considered in the second stage using the stochastic producers and quick dispatch units. To capture the uncertainties associated with electricity demand and wind speed, Latin hyperbolic sampling and fast forward selection methods are applied for scenario generation and reduction processes, respectively. The flexibility of the studied system is analyzed considering the variations of the key parameters of thermal units and the transmission line’s capacity. For this work, the modified IEEE 14-bus standard test system integrated with the components of the studied system is selected as the case study. After solving the problem, the maximum potential of clean energy production units is used in comparison with fossil fuel-based units through the optimal scheduling of the wind-thermal-hydropower-pumped storage system. Given the numerical results, reducing the flexibility of the system by reducing the ramp up/down parameters, increasing the minimum up/down parameters, and reducing the transmission line capacity has been led to increase of 6.47%, 7.3%, and 9.77% in the total energy cost, respectively.

Coupling an adaptive protection system with an energy management system for microgrids

Energy Management Systems (EMS) are control schemes in charge of defining the optimal scheduling of dispatchable units in a microgrid. Output set points from the EMS could be exploited in other control loops that may benefit from the microgrids expected behavior improving their operation. In this context, this paper proposes a novel coupled operation of an EMS with an Adaptive Protection System (APS), such that the information of the EMS serves to compute the parameters of the protective devices deployed in a microgrid. This aspect is formalized in the present proposal based on already developed APS and EMS frameworks. To evaluate the performance of the proposal, the microgrid installed in Huatacondo, north of Chile, was used as a test-bed. The obtained results indicate that considering the information provided to the APS form the EMS improves the overall performance of the microgrid in case of failure, despite the operating conditions. This also indicates that moving towards an integrated design of the EMS and APS might enhance the behavior (robustness) of a microgrid (as a whole) in case of failures or contingencies.

Availability estimation of wind power forecasting and optimization of day-ahead unit commitment

Due to the uncertainty of the accuracy of wind power forecasting, wind turbines cannot be accurately equated with dispatchable units in the preparation of a day-ahead dispatching plan for power grid. A robust optimization model for the uncertainty of wind power forecasting with a given confidence level is established. Based on the forecasting value of wind power and the divergence function of forecasting error, a robust evaluation method for the availability of wind power forecasting during given load peaks is established. A simulation example is established based on a power system in Northeast China and an IEEE 39-node model. The availability estimation parameters are used to calculate the equivalent value of wind power of the conventional unit to participate in the day-ahead dispatching plan. The simulation results show that the model can effectively handle the challenge of uncertainty of wind power forecasting, and enhance the consumption of wind power for the power system.

Real-Time Optimized Load Recovery Considering Frequency Constraints

A self-healing power grid can mitigate the impact of power outages and recover the affected area swiftly. To properly size the optimal amount and location of de-energized loads in the presence of both conventional and renewable sources, a precise optimization-based load recovery tool is proposed in this paper. The steady-state and transient behaviors of loads have been taken into account. The proposed tool contains three separate problems that interact with each other during the restoration period. The first-level problem determines the dispatching of generation units. The second-level problem comprises the non-linear algebraic equations, whose task is to provide the initial conditions for the third-level problem. Finally, the third-level problem intends to determine the optimal location and amount of load pickup through modeling the governor of generators as well as loads’ dynamic behaviors. To examine the effectiveness of the proposed tool, a modified IEEE 39-system under a partial blackout condition has been tested. The results are verified by a commercial time-domain simulation software (PSS/E) to demonstrate the accuracy of the proposed approach. Ultimately, the impact of wind turbine frequency control on the load recovery process has been studied under different penetration levels.

Intra- and Inter-Phase Power Management and Control of a Residential Microgrid at the Distribution Level

A power control and management strategy for islanded residential microgrids is presented. A three-phase residential microgrid can be viewed as three separate single-phase systems. Within each phase, there are PV systems, battery storage devices, and droop controlled dispatchable units. Connections among phases are made through back-to-back converters to allow for power transfer between the phases. Hence, voltage and frequency control and power management must be carried out at intra-phase and inter-phase levels. The intra-phase power control and management system uses a modified vector control with a multi-segment (P/f) droop strategy. In the case where such a balance cannot be maintained locally, the inter-phase power can be transferred through the back-to-back converters. Both intra-phase and inter-phase power control and management scenarios have been considered. To demonstrate the effectiveness of the strategy, a detailed three-phase residential microgrid model is developed in PSCAD/EMTDC environment, including switching models of the back-to-back converters. The results have shown that the proposed strategies can effectively maintain desired voltage and frequency profiles for each phase-wise and the overall microgrid, frequency, and power balance in each phase can be effectively managed for stable operation.

Application of Whale Optimization Algorithm for Environmental Constrained Economic Dispatch of Fixed Head Hydro-Wind-Thermal Power System

This work applies whale optimization algorithm for emission constrained economic dispatch of hydrothermal units including wind power. As the wind power has a characteristic of cleanliness and is renewable, this is convincing to include this for better operation of electric power system keeping in view both economic and environmental aspects. Hydrothermal scheduling integrated with wind power establishes a multi-objective problem that becomes economic emission hydro-thermal-wind scheduling problem while taking into consideration the cost due to wind uncertainty. Whale optimization algorithm is proposed to solve this emission constrained economic dispatch problem with competing objectives. This algorithm is recently developed and gives the best solution among other nature inspired algorithms. The objectives minimum generations as well as emission cost, both are optimized together including different constraints. A daily scheduling of all the three types of systems - hydro, thermal and wind is considered to evaluate the competency of this optimization technique to get a solution for this multi-objective problem. The experiments are carried out on two systems for determining the effectiveness of the suggested method. Besides, results found using the whale optimization technique have been compared with the results obtained from other evolutionary methods. From the comparison, it is experimentally justified that the whale optimization works faster and the cost of generation as well as cost of emission are lower than the other approaches.

Multi-objective economic emission dispatch of thermal power plants based on grey relational analysis and analytic hierarchy process

In this paper, mathematical models for a synthesized evaluation were established according to grey relational analysis and analytic hierarchy process. The models were used to select a power dispatch scheme considering hierarchies of material consumption, electrical efficiency, exergy efficiency, and environmental benefit. Four unit dispatch schemes, i.e., proportional fair allocation dispatch, conventional economic dispatch, economic emission dispatch, and economic emission dispatch with varied weights were investigated and compared. Analytic hierarchy process decision-making approach has been employed to find the optimal Pareto solution as the best tradeoff between cost and pollutant emission. The model indicated that the economic emission dispatch was preferred as the best option and could further reduce fuel consumption and pollutant emission, followed by the economic emission dispatch with varied weights. The assessment performed serves decision makers a valuable reference for policy making in the power dispatch sector.

The reliability and accuracy of operational system data in a nationwide helicopter emergency medical services mission database

AimThe aim of this study was to evaluate the reliability and accuracy of documentation in FinnHEMS database, which is a nationwide helicopter emergency service (HEMS) clinical quality registry.MethodsThis is a nationwide study based on written fictional clinical scenarios. Study subjects were HEMS physicians and paramedics, who filled in the clinical quality registry based on the clinical scenarios. The inter-rater -reliability of the collected data was analyzed with percent agreement and free-marginal multi-rater kappa.ResultsDispatch coding had a percent agreement of 91% and free-marginal multi-rater kappa value of 0.83. Coding for transportation or mission cancellation resulted in an agreement of 84% and free-marginal kappa value of 0.68. An agreement of 82% and a kappa value of 0.73 for dispatcher coding was found. Mission end, arrival at hospital and HEMS unit dispatch -times had agreements from 80 to 85% and kappa values from 0.61 to 0.73. The emergency call to dispatch centre time had an agreement of 71% and kappa value of 0.56. The documentation of pain had an agreement of 73% on both the first and second measurements. All other vital parameters had less than 70% agreement and 0.40 kappa value in the first measurement. The documentation of secondary vital parameter measurements resulted in agreements from 72 to 91% and kappa values from 0.43 to 0.64.ConclusionData from HEMS operations can be gathered reliably in a national clinical quality registry. This study revealed some inaccuracies in data registration and data quality, which are important to detect to improve the overall reliability and validity of the HEMS clinical quality register.

A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.

A wholesale electricity market design sans missing money and price manipulation

Using reliability differentiation via tolling agreements with diverse heat rates and fuel types, we propose an efficient wholesale electricity market design under demand and supply uncertainty. Mainly based on North America's market experience, our proposed design adopts an independent system operator's (ISO's) existing practice of least-cost dispatch of heterogeneous generation units, real-time energy price determination and capacity rationing. It solves the missing money problem of inadequate incentive for thermal generation investment, without requiring the ISO to operate centralized capacity auctions, make capacity payments, set high energy price caps, or subsidize market entry. It preempts independent power producers' price manipulation in the ISO's real-time market for energy, thus easing the ISO's burden of market monitoring. It suggests two-part pricing of end-use consumption and power demand of a load serving entity's retail customers, thus meaningfully linking the wholesale and retail markets. It is applicable to countries that have implemented wholesale competition or are in the process of doing so. Hence, its policy implication is that it should be considered in the ongoing debate of electricity reliability and market competition.

A Decentralized Composite Controller for Unified Voltage Control With Global System Large-Signal Stability in DC Microgrids

Bus voltage control is an essential issue in a DC microgrid (MG). In this paper, a decentralized composite controller (DCC) is proposed to unify two types of voltage controls, i.e., constant voltage mode and droop mode, and simultaneously realize global system large-signal stability of the MG. The main idea of the DCC is to partition the MG into dispatchable unit (DU) subsystems and the lumped load. A well-devised high gain observer is adopted to estimate the electrical coupling of a particular DU subsystem with other DUs and the load. Then, rather than dealing with the MG as a whole, the DCC is locally designed to offset the estimated coupling and stabilize the internal states of the DU subsystem. By doing so, each subsystem can be virtually decoupled from the MG and functions in an isolated fashion. When DU subsystems are interlinked, large-signal stabilization of the entire MG would be obtained by only guaranteeing the localized stability of individual subsystems. This stabilizing strategy has not been reported in the literature thus far. Relevant theoretical analyses are rigorously conducted by employing Lyapunov stability theorem. The effectiveness of the DCC has been verified by simulations. Experimentations show that the DCC enables faster system performance recovery and provides wider stability margin than the conventional PI controller.

Towards Balancing Market Integration: Conversion Process for Balancing Energy Offers of Central-Dispatch Systems

This paper presents a novel methodology for the conversion of balancing energy offers, submitted during the integrated scheduling process (ISP) of European central-dispatch systems, into replacement reserve (RR) standard products. The aim is to allow the integration of the balancing market of central-dispatch systems, where generating unit operating constraints and real-time dispatch lie in the responsibility of the TSO, with the pan-European balancing market mainly characterized by self-dispatch arrangements and portfolio-based market participation. To respect the diversified clearing rules of both systems, we propose a break-down of the one-shot RR clearing problem into four distinct steps: (a) a local RR quantity maximization process, where the maximum quantity of upward and downward RR balancing energy offered by each balancing service provider (BSP) is defined by the TSO subject to detailed unit operating limitations, (b) a local mandatory activation process, driven by binding unit constraints determined in the previous step, (c) a local conversion process, during which the ISP offers are converted into RR standard products, and (d) the purely economic RR clearing to obtain the activated volumes. Four different modeling approaches are constructed, the attained solution of which is compared with the one-shot techno-economic clearing benchmark currently applied in central-dispatch systems. The comparison indicates that the break-down process results coincide with the one-shot benchmark results, achieving identical social welfare and final unit dispatch, while additionally allowing less-costly Europe-wide cross-border activations of RR.

Participation of responsive electrical consumers in load smoothing and reserve providing to optimize the schedule of a typical microgrid

The task of energy management in electrical microgrids (MGs) is facing more difficulties with the integration of different schedulable components, e.g. responsive loads, energy storage systems (ESSs) and variety of generating units. In this paper the formulation of generation scheduling for a typical MG is done by the unit commitment problem (UCP) to minimize costs and emissions. Demand response program (DRP) is utilized to achieve two goals, i.e. to reshape and smooth the load profile, and also for providing reserve capacity along with dispatchable units. The aforementioned tasks are carried out by differential evolution (DE) algorithm which is a simple and powerful optimizer. In order to deal with multiple criteria decision making in the energy management problem, an algorithm called fuzzified modified (FM) e-constraint is proposed as a non-Pareto based multi-objective solver. The optimal scheduling plan tends to benefit from different electricity prices in day-ahead market and also from the ESS to achieve optimal scheduling results. It is shown that in single objective cases, committing less generating units simultaneously is favorable, as opposed to multi-objective one in which more available units are tuned on at a lower generating power. Meanwhile in all cases, the responsive loads are mostly aggregated in off-peak periods to help peak load shaving. Finally it is verified that the proposed multi-objective approach is able to find an intermediate solution between the two conflicting functions so that as a trade-off each objective is optimized to some extent.

Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage

A Stochastic Model for the Unit Commitment (SUC) problem of a hybrid microgrid for a short period of 24 h is presented. The microgrid considered in the problem is composed of a wind turbine (WT), a photovoltaic plant (PV), a diesel generator (DE), a microturbine (MT) and a Battery Energy Storage System (BESS). The problem is addressed in three stages. First, based on the historical data of the demanded power in the microgrid, an ARMA model is used to obtain the demand prediction. Second, the 24-h-ahead SUC problem is solved, based on generators’ constraints, renewable generation and demand forecast and the statistical distribution of the error in the demand estimation. In this problem, a spinning reserve of the dispatchable units is considered, able to cover the uncertainties in the demand estimation. In the third stage, once the SUC problem has been solved, a case study is established in real time, in which the demand estimation error in every moment is known. Therefore, the objective of this stage is to select the spinning reserve of the units in an optimal way to minimize the cost in the microgrid operation.

Quantifying Power System Operational Reliability

Rising uncertainty in power systems due to ongoing system and operational changes has led to increasing risks in operating a system in a reliable and economic manner. There are growing reliability concerns with the widely used methods, such as the N-1 criterion, to determine operating reserve requirement during unit commitment, and the economic load dispatch method to allocate regulating margin to respond to disturbances. These deterministic methods do not consider the uncertainty or stochastic nature of power systems and are often inadequate to maintain the required operating reliability of modern power systems. This paper introduces a reliability index, designated as the committed generators’ response risk (CGRR) that can be used to maintain a specified level of operating reliability. A probabilistic method to evaluate the CGRR is presented and validated using a Monte Carlo simulation technique. An application of the new index and the methodology is illustrated using the IEEE Reliability Test System. The CGRR provides a comprehensive operating risk measurement of the committed generation until further assistance is available to support the system, and therefore helps operators in decision making for unit commitment and dispatch of the generating units to meet the projected load in the short future time.

Representation of Balancing Options for Variable Renewables in Long-Term Energy System Models: An Application to OSeMOSYS

The growing complexity and the many challenges related to fast-changing and highly de-carbonised electricity systems require reliable and robust open source energy modelling frameworks. Their reliability may be tested on a series of well-posed benchmarks that can be used and shared by the modelling community. This paper describes and integrates stand-alone, independent modules to compute the costs and benefits of flexible generation options in the open source energy investment modelling framework OSeMOSYS. The modules are applied to a case study that may work as a benchmark. The whole documentation of the modules and the test case study are retrievable, reproducible, reusable, interoperable, and auditable. They create a case to help establish a FAIR-compliant, user-friendly, and low-threshold model and data standards in modelling practices. As is well known, one of the options for balancing high shares of variable renewables is flexible power generation by dispatchable units. The associated costs need to be considered for short-term operational analyses and for long-term investment plans. The added modules contribute to extending the modelling capacity by introducing (a) costs of ramping, (b) non-linear decrease of efficiency at partial load operation, and (c) refurbishment of existing units in the cost minimisation objective function of OSeMOSYS. From application to the test case study, two main insights are drawn: costs of ramping and decreased partial load efficiency may influence the competitiveness of generation technologies in the provision of reserve capacity; and refurbishment of existing units may represent attractive investment options for increasing flexibility. Both effects are also seen in the long-term and may impact infrastructure investment decisions to meet decarbonisation targets. These effects would not be captured without the introduction of the modules.

Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation

This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.

Minimizing Unserved Energy Using Heterogeneous Storage Units

This paper considers the optimal dispatch of energy-constrained heterogeneous storage units to maximise security of supply. A policy, requiring no knowledge of the future, is presented and shown to minimise unserved energy during supply-shortfall events, regardless of the supply and demand profiles. It is accompanied by a graphical means to rapidly determine unavoidable energy shortfalls, which can then be used to compare different device fleets. The policy is well-suited for use within the framework of system adequacy assessment; for this purpose, a discrete time optimal policy is conceived, in both analytic and algorithmic forms, such that these results can be applied to discrete time systems and simulation studies. This is exemplified via a generation adequacy study of the British system.

Coordinated Operation of Electricity and Natural Gas Systems: A Convex Relaxation Approach

The variability in the generation dispatch of the natural gas generation units will lead to fluctuation in natural gas demand profile that could further jeopardize the security of the natural gas network. The coordinated operation of electricity and natural gas infrastructure systems would help to improve the security and reliability measures in both infrastructure systems and mitigate the risk of demand curtailment. The electricity and natural gas network operation problems are non-convex mixed-integer nonlinear programming problems that are hard to solve in polynomial time. The non-convex feasible regions are formed by the Weymouth constraint and the introduced binary commitment decision variables in the natural gas and electricity network operation problems, respectively. This paper utilized a sparse semidefinite programming (SDP) relaxation to procure the optimal solution for the coordinated operation of electricity and natural gas networks. The presented algorithm leverages the sparseness of the natural gas network to construct several small matrices of lifting variables that are used to form a tight and traceable SDP relaxation. A set of valid constraints that tighten the relaxation ensures the exactness of the solution procured from the relaxed problem. The effectiveness of the presented approach is shown in case studies.

A Wholesale Electricity Market Design Sans Missing Money and Price Manipulation

Using reliability differentiation via tolling agreements with diverse heat rates and fuel types, we propose an efficient wholesale electricity market design under demand and supply uncertainty. Mainly based on North America’s market experience, our proposed design adopts an independent system operator’s (ISO’s) existing practice of least-cost dispatch of heterogeneous generation units, real-time energy price determination and capacity rationing. It solves the missing money problem of inadequate incentive for generation investment, without requiring the ISO to operate centralized capacity auctions, make capacity payments, set high energy price caps, or subsidize market entry. It preempts independent power producers’ price manipulation in the ISO’s real-time market for energy, thus easing the ISO’s burden of market monitoring. It suggests two-part pricing of end-use consumption of a load serving entity’s retail customers, meaningfully linking the wholesale and retail markets. It is applicable to countries that have implemented wholesale competition or are in the process of doing so. Hence, its policy implication is that it should be considered in the ongoing debate of electricity reliability and market competition.