Multi-Level Cooperative Scheduling Based on Robust Optimization Considering Flexibilities and Uncertainties of ADN and MG

This paper develops the coordination structure and method for utilizing flexibilities in a Micro-Grid (MG), an Active Distribution Network (ADN) and a Transmission Grid (TG), which can play an essential role in addressing the uncertainties caused by renewable energy power generation (REPG). For cooperative dispatching, both flexibilities and uncertainties on the interface of MG–ADN and ADN–TG are portrayed in unified forms utilizing robust optimization (RO), based on the modified equipment-level model of flexible resources. The Constraint-and-Column Generation method is adopted to solve the RO control problems. Simulations on the modified IEEE case-6 and case-33 systems are carried out. The results suggest that the proposed algorithm can exploit flexible resources in both an MG and an ADN, improving the economy and promoting REPG consumption within each level (MG, ADN and TG) while reducing uncertainties and providing flexibilities for superior operators.

Orderly grid-connected cooperative scheduling control strategy based on distributed energy storage for electric vehicles

Aiming at the problem that large-scale disorderly grid connection of electric vehicles negatively affects grid operation and causes a large amount of abandoned wind and abandoned light, an orderly grid connection cooperative scheduling control strategy based on distributed energy storage of electric vehicles is proposed. The strategy takes the charging and discharging price as the lever to guide the users to charge and discharge in an orderly manner, takes the optimal economics on the user side and the optimal cost of power generation on the grid side as the objective function, and uses linear weighting normalization to convert the multi-objective function into a single objective function for simulation solution. The simulation results show that the effect of peak shaving and valley filling can be achieved on the basis of satisfying users’ demand, and renewable energy can be effectively consumed.

Dynamic immune cooperative scheduling of agricultural machineries

Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

Optimal Operation of Isolated Micro-Grids-cluster Via Coalitional Energy Scheduling and Reserve Sharing

Microgrids (MG) cluster are isolated from the utility grid but they have the potential to achieve better techno-economic performance by using joint energy and reserve sharing among MGs. This paper proposes a techno-economic framework for the optimal operation of isolated MGs-cluster by scheduling cooperative energy sharing and real-time reserve sharing for ancillary services based on the cooperative game theory. In the day-ahead scheduling, a coalitional sharing scheme is formulated as an adjustable robust optimization (ARO) problem to optimally schedule the energy and reserves of distributed generators (DGs) and energy storage systems (ESSs), thereby responding to the uncertainties of photovoltaic systems, wind turbines, and loads. These uncertainties are the main reason for power system imbalance which is mitigated by regulating the frequency in real-time and a dynamic droop control process is used to realize the reserves in a distributed manner. This control process is embedded into the ARO problem, which is formulated as an affine ARO problem and then transformed into a deterministic optimization problem that is solved by off-shore solvers Apart from the reduction in the operation cost, the frequency restoration can be improved jointly, resulting in the coupled techno-economic contribution of the MGs in the coalition. The contribution of each MG is quantified using shapely value, a cooperative game approach. Simulations are conducted for a case study with 4 MGs and the results demonstrate the merits of the proposed cooperative scheduling scheme.

A Formal, Resource Consumption-Preserving Translation from Actors with Cooperative Scheduling to Haskell*

We present a formal translation of a resource-aware extension of the Abstract Behavioral Specification (ABS) language to the functional language Haskell. ABS is an actor-based language tailored to the modeling of distributed systems. It combines asynchronous method calls with a suspend and resume mode of execution of the method invocations. To cater for the resulting cooperative scheduling of the method invocations of an actor, the translation exploits for the compilation of ABS methods Haskell functions with continuations. The main result of this article is a correctness proof of the translation by means of a simulation relation between a formal semantics of the source language and a high-level operational semantics of the target language, i.e., a subset of Haskell. We further prove that the resource consumption of an ABS program extended with a cost model is preserved over this translation, as we establish an equivalence of the cost of executing the ABS program and its corresponding Haskell-translation. Concretely, the resources consumed by the original ABS program and those consumed by the Haskell program are the same, considering a cost model. Consequently, the resource bounds automatically inferred for ABS programs extended with a cost model, using resource analysis tools, are sound resource bounds also for the translated Haskell programs. Our experimental evaluation confirms the resource preservation over a set of benchmarks featuring different asymptotic costs.