Dispatch Of Resources Research Articles

Hierarchically Coordinated Voltage Control in Seaport Microgrids Considering Optimal Voyage Navigation of All-Electric Ships

The uncoordinated scheduling and control of all-electric ships (AESs) will impose great challenges on voltage profiles in seaport microgrids. This article proposes a three-stage hierarchically coordinated voltage control strategy in seaport microgrids considering the optimal voyage navigation and scheduling of AES. In stage one, we first propose an arrival time estimation method for AES to reduce the potential voltage violation risk in different seaport microgrids. The optimal power flow (OPF) is then performed in each microgrid to determine the day-ahead dispatch of on-load tap changer (OLTC) and inverter-based renewable energy resources (RESs). In stage two, the optimal route planning is carried out based on the sea states and the Dijkstra algorithm. The voyage and generation scheduling of AES are formulated as optimization problems and solved by CPLEX solvers. In stage three, a real-time local voltage control strategy is performed to mitigate fast voltage violations. To fully exploit the voltage support capability of energy storage systems (ESSs) on berthed-in AES, both constant charging mode and voltage control mode are designed to regulate voltages while ensuring sufficient SOC levels of ESS when leaving the seaport. The simulation results verify the effectiveness of the proposed method both in saving the generation cost of AES and improving voltage profiles in seaport microgrids.

DRL-Assisted Topology Identification and Time Synchronization for PIoT-Empowered Distributed Renewable Resource Dispatch

DRL-Assisted Topology Identification and Time Synchronization for PIoT-Empowered Distributed Renewable Resource Dispatch

Assessment of the Modeling of Demand Response as a Dispatchable Resource in Day-Ahead Hydrothermal Unit Commitment Problems: The Brazilian Case

Modern power systems have experienced large increases in intermittent and non-dispatchable sources and a progressive reduction in the size of hydro reservoirs for inflow regularization. One method to mitigate the high uncertainty and intermittency of the net load is by Demand Response (DR) mechanisms, to allow a secure and reliable system dispatch. This work applied a mixed integer linear programming formulation to model DR as a dispatchable resource in the day-ahead hydrothermal scheduling problem, taking into account minimum load curtailment constraints, minimum up/down load deduction times, as well as piecewise linear bid curves for load shedding in eligible loads. The methodology was implemented in the official model used in Brazil and tested in large-scale problems to obtain the optimal daily dispatch and hourly pricing. The results show the positive impact of dispatchable DR loads in cost reduction and in mitigating peak values of energy prices, even for predominantly hydro systems, helping to preserve the reservoir levels and increasing the security of the supply in the future.

Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience

In this paper, a post-disaster distribution system repair and restoration (DSRR) strategy is proposed to improve distribution system resilience. The DSRR strategy is formulated as a two-stage optimization. The first stage is a comprehensive co-optimization of repair crew scheduling, dynamic network reconfiguration, and distributed energy resource (DER) dispatch based on the forecast load profile. The goal is to minimize the accumulative operating cost caused by the load reduction payment as well as DER operating cost. In particular, since the number of available repair crews is usually smaller than the number of faulted lines after a disaster event, the DSRR strategy determines the optimal scheduling for repairing faulted lines. The second stage is a re-dispatch of the DER power output and load shedding based on the real-time load demand of each bus. The proposed algorithm is validated by case studies of the IEEE 33-bus and 123-bus test systems. We consider those scenarios in which faults occur in multiple heavy-loaded feeders. The simulation results demonstrate that the DSRR strategy effectively coordinate the repair scheduling, network reconfiguration and load shedding to minimize the operating cost.

Modeling the risk-based decisions of the microgrid in day-ahead energy and reserve markets considering stochastic dispatching of electrical and thermal energy storages

• The MGO’s bids in the markets are optimized with dispatching of local resources. • Uncertainties of electrical demand and power generation of PV are modeled. • A robust two-stage stochastic optimization model is developed. • The most capacity of electrical energy storage is used to provide reserve capacity. • With increasing the robust parameter, the MGO’s cost increases. In this paper, the electrical and thermal energy management problem of a micro-grid operator (MGO) is addressed under uncertainties aiming at participating in the day-ahead energy and reserve markets. For this purpose, a robust two-stage stochastic model is developed to protect the first stage MGO’s decisions, i.e., its bids in the energy and reserve markets, against the uncertainty of the real-time energy market price. This is done through stochastic dispatching of the MG resources which includes the electrical and thermal energy storages and the combined heat and power unit as the second-stage decisions. The results showed that the MGO’s expected total cost decreases when it participates in both the energy market and the reserve market in comparison with the case it only participates in the energy market. Also, the risk-based behavior of the MGO showed that increasing the robust parameter decreases the reserve provided for the market and the net power trading with the market. However, the proposed robust two-stage stochastic model leads to a smaller reduction of the MGO’s first-stage decisions in the worst case in comparison with the conventional methods, i.e. deterministic and probabilistic ones. This issue proves the effectiveness of the proposed approach to protect the MGO’s decisions against the uncertainties.

Coordinating Energy Resources in an Islanded Microgrid for Economic and Resilient Operation

Microgrids with advanced management and control strengthen power system reliability and resilience. In normal operation, economy and reliability are the objectives. However, for adverse events, economy would not be the top concern and focus moves to resiliency and providing energy to critical loads. The resource dispatch strategy in normal economic mode would not be suitable for resilient mode given adverse events. In this work, an energy resource coordination strategy is proposed for a hydro–diesel-battery islanded microgrid, which improves distribution system resiliency by preevent control and switching to resilient mode from economic mode. Resiliency metric is used to verify and quantify the effectiveness of the selected strategy among available ones. Real-time resiliency management system (RT-RMS), a real time resilience management and visualization tool is developed and the resiliency driven resource coordination is integrated into it. Finally, a 14-nodes isolated power system model and a simplified 55-nodes real-world islanded microgrid model are used for validation and demonstrate the effectiveness of proposed resource coordination method.

A Low-Carbon Dispatch Strategy for Power Systems Considering Flexible Demand Response and Energy Storage

The consumption of traditional fossil energy brings inevitable environmental protection problems, which also makes the low-carbon transition in industrial development imminent. In the process of low-carbon transition, the power industry plays a very important role. However, the large-scale integration of renewable energy resources such as wind power and photovoltaic brings new characteristics to power system dispatch. How to design a dispatch strategy that considers both low-carbon demand and economic cost has become a major concern in power systems. The flexible resources such as demand response (DR) and energy storage (ES) can cooperate with these renewable energy resources, promoting the renewable energy generation and low-carbon process. Thus, a low-carbon dispatch strategy for power systems considering flexible DR and ES is proposed in this article. First, models of DR and ES based on their behavior characteristics are established. Then, a carbon emission index is presented according to China’s Clean Development Mechanism (CDM). Finally, the low-carbon dispatch strategy for power systems is proposed through the combination of the carbon emission index and flexible resource dispatch models. The simulation results show that the proposed dispatch strategy can significantly improve wind power consumption and reduce carbon emission.

Technical Note—Bifurcating Constraints to Improve Approximation Ratios for Network Revenue Management with Reusable Resources

In “Bifurcating Constraints to Improve Approximation Ratios for Network Revenue Management with Reusable Resources,” Baek and Ma provide a unified framework for dynamically allocating reusable resources over time in the general network revenue management setting. That is, each incoming query is allowed to request an arbitrary combination of resources for different unknown durations, capturing applications like cloud computing and the dispatching of human resources. Through this framework, they derive simple algorithms that achieve the same approximation ratios as existing algorithms, which only allow queries to request one resource. Their framework also suggests a meta-algorithm for “bifurcating” the resources depending on the network structure and then using two different algorithms for controlling the capacities of two different groups of resources. This contrasts existing “one-size-fits-all” approaches to network revenue management and is demonstrated to improve both theoretical guarantees and empirical performance over a wide variety of instances.

Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data

The power system with high penetration of wind power faces a great challenge for system dispatch due to the high volatility and intermittency of the wind power. This work proposes a day-ahead optimal dispatch model which is formulated for a power system with thermal power, hydropower, and controllable load as dispatchable resources. According to the anti-peak regulation, the system dynamic power regulation margin model considering adjacent time periods is established to address the uncertainty in the fluctuation rate of net load power, and to sufficiently use the dispatchable resources to reduce wind curtailment. However, in some circumstances, curtailing wind has to be considered to ensure secure operation of the system and maintain the economic goal from the total cost point of view. The risk of curtailing wind is formulated using conditional value at risk (CVaR), and is minimized as part of the total operating cost. Another objective function of the proposed dispatch model is to maximize the power regulation margin. Uncertainty in the fluctuation rate of net load power is modelled for different weather conditions using moment-based ambiguity set. The moment information is obtained from large amount of historical data using clustering methodologies. The proposed optimal dispatch model with uncertainty and CVaR formulation is reformulated and solved under the distributionally robust conditional value at risk (DRCVaR) framework. The model is transformed into a semi-definite programming problem through the duality theory and can be solved efficiently by commercial solvers. Simulation results show that the proposed dispatch model can effectively strengthen wind power absorption, ensure secure operation, and improve the robustness of the dispatch strategy facing the uncertainty from the wind power.

Three-level multimodal transportation network for cross-regional emergency resources dispatch under demand and route reliability

Global emergencies, ranging from natural disasters and public health emergencies to terrorist attacks, have been more frequent of late. In the face of large-scale emergencies, it is difficult for an intra-regional emergency resource dispatch mode to meet the subsequent requirements. Essentially, the interruption of local routes leads to uncertainty in resource demand and transportation time. In light of this, this study defines route reliability to describe the possibility of interruption and establishes a functional relationship between route reliability and delay. We further propose a model, using an ant colony optimization design, as a solution to the problem. Using the 2008 Wenchuan earthquake in China as a numerical experiment, the target value of generalized cost was calculated to be 0.3819, which was 9.53% lower than that obtained by a two-level transportation network and 5.71% lower than that obtained by a three-level single transportation network. Additionally, dispatch plans with different route reliability were compared. In the case of pessimistic route reliability, the resources transported by highways were found to shift to railways for long distances and to aviation for short distances. In contrast, for optimistic route reliability, highway transportation with moderate cost and speed was preferred.

Resilience-Oriented Dynamic Distribution Network With Considering Recovery Ability of Distributed Resources

The installation of distributed generations (DGs) and energy storages (ESs) in active distribution networks (DNs) could help to increase the resilience of the system. When large scale power outages of urban areas occur, these distributed resources could be used to recover partial, especially the most important loads. However, the determination of boundaries in the distribution networks and the strategy to recover loads are crucial to the overall DN reliability. This paper proposes a framework for resilient distribution network (RDN) to utilize all dispatchable resources to enhance the recovery ability under severe incidents like the complete failure of a substation. Firstly, the idea of restoration areas (RAs) planning with partial recovery is introduced. Each restoration area is schemed to utilize ES, DG and load transfer (LT) to recover loads as much as possible with considering the load importance. The combination weighting method is applied to evaluate the load priority. After that, a dynamic recovery strategy is designed that is valid regarding the actual operation condition and the reliability requirements in distribution system. The flexible boundaries of the RAs can expand or shrink to deal with the uncertainty of power sources output. Furthermore, the load regulation or demand response (DR) is performed to maintain power balance and enhance the overall recovery rate. Finally, illustrative results on IEEE RBTS test systems verify the effectiveness of the proposed model.

Optimal stochastic scheduling in microgrids under managed risk

This paper addresses the stochastic optimal day-ahead microgrid (MG) energy resources scheduling, considering the uncertain load, price of electricity, and generated electrical power by wind and solar units. Moreover, the vehicle-to-grid (V2G) implementation, load curtailment cost, and spinning reserve requirements are modeled to make the results more practical and applicable. Furthermore, the price elasticity of supply is considered to explore the relation between V2G capability and the optimization process. The stochastic energy resources scheduling problem is formulated in a two-level optimization framework. The unit commitment of dispatchable resources is analyzed in the upper level, and the lower level is formulated as a scenario-based two-stage stochastic programming problem that minimizes the operation cost of MG considering all the constraints. The risk of attaining unfavorable high costs of MG scheduling is considered using the variance approach. The generated scenarios are reduced by using the backward reduction method for each uncertain variable at each hour, independently. The artificial intelligent-based methods, including differential evolution algorithm (DEA), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES) are applied to solve the problem. The effectiveness of the proposed approaches is confirmed by simulations on a modified 13-bus IEEE test system, in two cases of neglecting the risk and including the managed risk by applying the real-world data. The results confirmed the better performance of CMAES for solving such optimization problems.

Solar photovoltaic dispatch margins with stochastic unbalanced demand in distribution networks

The increase in the generation capacity of the variable photovoltaic units introduces new challenges to the operation of the unbalanced three-phase distribution networks. In this paper, a two-stage optimization problem is formulated to identify the feasible dispatch margins of photovoltaic generation considering the distribution network operation constraints. The proposed problem is solved using the column-and-constraint generation approach. The distribution network constraints are formulated as second-order cone constraints. The uncertainty in the forecasted demand and maximum photovoltaic generation as well as the unbalanced operation of the distribution network is considered in the proposed approach. The dispatch margins of photovoltaic generation are quantified considering the worst-case realization of demand in the distribution network. The impacts of energy storage and the ramping limits of the dispatchable generation resources on the dispatch margins of photovoltaic generation are addressed in this network. The dispatch margins of photovoltaic generation are quantified in the modified IEEE 13-bus system. It is shown that enforcing the ramping rates for the dispatchable units will increase the lower dispatch margins of photovoltaic generation, and leveraging energy storage increases the difference between the lower and upper photovoltaic dispatch margins.

A cross-platform OpenVX library for FPGA accelerators

FPGAs are an excellent platform to implement computer vision applications, since these applications tend to offer a high level of parallelism with many data-independent operations. However, the freedom in the solution design space of FPGAs represents a problem because each solution must be individually designed, verified, and tuned. The emergence of High Level Synthesis (HLS) helps solving this problem and has allowed the implementation of open programming standards as OpenVX for computer vision applications on FPGAs, such as the HiFlipVX library developed exclusively for Xilinx devices. Although with the HiFlipVX library, designers can develop solutions efficiently on Xilinx, they do not have an approach to port and run their code on FPGAs from other manufacturers. This work extends the HiFlipVX capabilities in two significant ways: supporting Intel FPGA devices and enabling execution on discrete FPGA accelerators. To provide both without affecting user-facing code, the new carried out implementation combines two HLS programming models: C++, using Intel’s system of tasks, and OpenCL, which provides the CPU interoperability. Comparing with pure OpenCL implementations, this work reduces kernel dispatch resources, saving up to 24% of ALUT resources for each kernel in a graph, and improves performance 2.6 × and energy consumption 1.6 × on average for a set of representative applications, compared with state-of-the-art frameworks.

Analyzing carbon emissions policies for the Bolivian electric sector

A transition of the Bolivian power sector towards a renewable energy dominated system has been inhibited by a series of laws and policies including heavy subsidies for power generation using domestic natural gas. Within this context, alternative techno-economic scenarios are designed based on key characteristics of the system, and a series of six policy levers are used to analyze impacts on the development of the power sector. The energy-system optimization modeling framework OSeMOSYS is utilized to analyze power sector transition pathways. Techno-economic characteristics and policies are combined to develop bracketing scenarios for the future energy system, contrasting business-as-usual with an ambitious renewable energy policy scenario.Results from the analyzed scenarios show that achieving significant reductions of GHG emissions in the Bolivian electric system will heavily depend on:1) reducing the artificial competitiveness of thermal power plants through subsidies, but also a price on carbon emissions; 2) banning high impact power plants (mainly very large hydropower plants); and 3) defining clear long-term objectives for the participation of renewables in the system, starting with objectives in current short-term plans. By examining several scenarios, relative system costs as a function of emissions reductions are determined as well. For high penetration of variable renewable energy, addition of storage will eventually be needed as dispatchable renewable resources are limited.

Forecasting the Amount of Recyclables Using an Improved Differential Evolution-based Neural Network

Forecasting the amount of recyclables accurately affects succeeding activities such as the allocation and dispatch of resources and the manufacturing of recycled products, and thus plays an essential role in building a successful municipal recycling system. In this paper, the logistic chaotic map and differential evolution algorithm are used to provide optimal initial weights and thresholds for the back-propagation neural network to improve its prediction accuracy. A case study with real data collected from four communities in Shanghai demonstrates the performance of the proposed model. Results show that the proposed model can reduce the average mean absolute error by 27.17% compared to the traditional back-propagation neural network model.

Communication satellite resource scheduling based on improved whale optimization algorithm

Under the background of increasing pressure of satellite communication support, reasonable and efficient dispatch of communication satellite resources is an important means to improve the utilization efficiency of communication resources. Aiming at the resource scheduling task requirements of geostationary orbit communication satellite system, communication satellite resource scheduling (CSRS) model is established first of all, based on this, advances a kind of CSRS method based on improved whale optimization algorithm. In this method, the detection and search strategy is proposed and the crossover mutation operator is used to avoid the algorithm falling into local optimum. Simulation results show that IWOA can effectively improve the quality and stability of satellite resource scheduling.

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes, and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. Consequently, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems, and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence, and shorter running time.

California's approach to decarbonizing the electricity sector and the role of dispatchable, low-carbon technologies

California's Senate Bill 100 establishes a goal to reach a zero-carbon grid by 2045, which opens the door for currently available low-carbon, dispatchable energy sources to contribute to California's decarbonization. This study utilizes a detailed capacity expansion and dispatch model of California to assess the role that low-carbon, dispatchable resources can have for California's energy future. The results show that including dispatchable, low carbon resources result in a significantly cheaper decarbonized system. In addition, leaving the optionality for more resources provides less uncertainty for future energy systems costs across varying technology cost estimates, weather patterns, and operational constraints. While California's 2045 zero-carbon grid policy establishes a strong premise for optionality, other regulatory updates will be necessary to deploy low-carbon, dispatchable resources efficiently.

A Probabilistic and Value-Based Planning Approach to Assess the Competitiveness between Gas-Fired and Renewables in Hydro-Dominated Systems: A Brazilian Case Study

The main challenge with the penetration of variable renewable energy (VRE) in thermal-dominated systems has been the increase in the need for operating reserves, relying on dispatchable and flexible resources. In the case of hydro-dominated systems, the cost-effective flexibility provided by hydro-plants facilitates the penetration of VRE, but the compounded production variability of these resources challenges the integration of baseload gas-fired plants. The Brazilian power system illustrates this situation, in which the development of large associated gas fields economically depends on the operation of gas-fired plants. Given the current competitiveness of VRE, a natural question is the economic value and tradeoffs for expanding the system opting between baseload gas-fired generation and VRE in an already flexible hydropower system. This paper presents a methodology based on a multi-stage and stochastic capacity expansion model to estimate the optimal mix of baseload thermal power plants and VRE additions considering their contributions for security of supply, which includes peak, energy, and operating reserves, which are endogenously defined in a time-varying and sized in a dynamic way as well as adequacy constraints. The presented model calculates the optimal decision plan, allowing for the estimation of the economical tradeoffs between baseload gas and VRE supply considering their value for the required services to the system. This allows for a comparison between the integration costs of these technologies on the same basis, thus helping policymakers and system planners to better decide on the best way to integrate the gas resources in an electricity industry increasingly renewable. A case study based on a real industrial application is presented for the Brazilian power system.