Robust Scheduling of Hydro-Driven Drinking Water

What is it about?

Nowadays, due to high energy cost, global climate changes, and growing trend of energy usage, renewable energy sources (RESs) are getting more attention in generation sector of power systems. Among different technologies of renewable energy resources, solar and wind energies have been adopted extensively. However, there are still more potentials about renewable resources which need more attention. In this regard, million miles of gravity-fed water pipelines around the world have resulted in introducing a new renewable energy from in-pipe hydropower. In this technology, the turbines that are installed in drinking water pipelines spin as water passes through them, converting excess head pressure into electricity. Since the water flow rate in pipeline is an uncertain parameter, the output generation power has uncertainty. The robust optimization method is an easier way to model uncertainties than probabilistic and fuzzy methods. This method, without considering the distribution function of uncertain parameters, determines the worst situation of uncertainties in the defined bound and tries to obtain the best solution immunizing against it. This chapter presents a mathematical robust model for a microgrid (MG) to obtain the battery energy storage system (BESS) charge/discharge scheduling and the exchange of power with upstream network considering the uncertainties of electricity market price, demand, and water flow. In this regard, a min-max problem, which is modeled as a bi-level optimization problem, is developed and is solved in two iterative steps. In the first step, a genetic algorithm (GA) is applied to obtain the worst case wherein uncertain parameters are determined such that MG energy procurement cost is maximized. Then, a mixed-integer linear problem is solved to minimize the energy procurement cost over MG decision variables considering the values determined in the first step. The steps are iterated to converge to the best solution. The proposed algorithm is implemented on an MG, and the results are reported.

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Why is it important?

The robust optimization method is an easier way to model uncertainties than probabilistic and fuzzy methods. This method, without considering the distribution function of uncertain parameters, determines the worst situation of uncertainties in the defined bound and tries to obtain the best solution immunizing against it. This chapter presents a mathematical robust model for a microgrid (MG) to obtain the battery energy storage system (BESS) charge/discharge scheduling and the exchange of power with upstream network considering the uncertainties of electricity market price, demand, and water flow. In this regard, a min-max problem, which is modeled as a bi-level optimization problem, is developed and is solved in two iterative steps. In the first step, a genetic algorithm (GA) is applied to obtain the worst case wherein uncertain parameters are determined such that MG energy procurement cost is maximized. Then, a mixed-integer linear problem is solved to minimize the energy procurement cost over MG decision variables considering the values determined in the first step.