Optimal operation of multi-storage tank multi-source system based on storage policy

A two-stage method is developed to solve a new class of multi-storage tank multi-source (MTMS) systems. In the first stage, the optimal storage policy of each tank is determined according to the electricity tariff, and the ground-level storage tank is modeled as a node. In the second stage, the genetic algorithm, combined with a repairing scheme, is applied to solve the pump scheduling problem. The objective of the pump scheduling problem is to ensure that the required volume is adequately provided by the pumps while minimizing the operation cost (energy cost and treatment cost). The decision variables are the settings of the pumps and speed ratio of variable-speed pumps at time steps of the total operational time horizon. A mixed coding methodology is developed according to the characteristics of the decision variables. Daily operation cost savings of approximately 11% are obtained by application of the proposed method to a pressure zone of S. Y. water distribution system (WDS), China.

Comparative experimental analysis on various picking types under dedicated and random storage assignments for automated storage and retrieval systems

Product storage policy, single picking volume and picking routing are the three factors of vital importance that affect the efficiency of a crane to pick goods in automated storage and retrieval systems(AS/RS). Comparative experiments on picking efficiency were conducted targeting picking operation with order of 1 to 20. Based on dedicated and random storage policies, 4 picking methods of patching-based, S-type, return-type and optimized-type routes were used and compared in the experiments. The results show that either the dedicated policy or the random policy was applied, crane worked most efficiently with optimizedtype route, followed by S-type path, patching-based path, and return-type path. When the number of orders in a single picking is larger(more than 5), the random storage policy is preferable to the dedicated policy.

China’s role in scaling up energy storage investments

Accelerating the planning and development of a new power system that is more renewable energy-based is a strategic priority of achieving“dual carbon”goals(peaking carbon emissions before 2030 and becoming carbon neutral before 2060)in China.The large-scale development of energy storage technologies will address China’s flexibility challenge in the power grid,enabling the high penetration of renewable sources.This article intends to fill the existing research gap in energy storage technologies through the lens of policy and finance.Results indicate that policy uncertainties in renewable energy might undermine domestic investor confidence in energy storage technologies,while insufficient economic incentives may crowd out private sector participation.Drawing on international best practices,blended concessional finance,supported by development partners,can play a significant role in closing energy storage financing gaps in China and in countries of the Belt and Road Initiative(BRI).To deliver on China’s domestic and international climate commitments,this article makes three policy recommendations:(1)moving forward with a carbon pricing agenda that incentivizes energy storage investments in China;(2)tapping the potential of the domestic capital market to close financing gaps for novel energy storage technologies;(3)scaling up energy storage supply chains in BRI countries through multilateral cooperation.