Short-term Optimization And Its Risk Management Of Renewable Generations Under Different Operating Environments

Short operation is one of the most critical factors for optimizing the utilization ofintermittent renewable energy generations (RGs). But the uncontrollable characteristic ofRGs adds risks on their short-term operation, the decision-makers must take those risksinto account in order to make a good plan. Because the risk takers and managers are not thesame under different operating environments, the optimization of short-term operation ofRGs should be modeled and solved depending on different risk takers. Furthermore, therapid development of controllable loads in recent years (such as energy storage, electricvehicles, smart home, etc.) offers more available resources for optimizing RGs, so thisthesis carries researches on short-term optimization and its risk management of RGs fordifferent operating environments with the consideration of controllable loads.Risk takers of RGs are the whole system and the risk manager is the operators ofpower grid when governments force the power system to meet generating demand of thoseRGs. Both non-market and RG priority market are belonged to this category and the firstpart of the thesis focuses on the non-markets environment. Since the investment ofcontrollable loads in this environment are mainly due to the power grid, such as the batteryswitch stations of electric vehicles, intelligent community and energy storage stations inour country are most invested by the State Grid. So short-term optimization of RGs in thisenvironment is actually the short-term optimization of power system with RGs andcontrollable loads. From the system’s view, it proposes a chance reserve constrainedoptimization model for short-term operation of those power systems by adding the storages,electric vehicles and other controllable loads into the traditional economic operation modeland utilizing the confidence level of chance reserve constraint as the risk measure of theschedule plan. These researches can offer an optimization and risk management tool forpower system with RGs and controllable loads.The risk taker is still the whole power system in RG priority markets. The decision-making of energy markets is similar with the non-market environment, so thesecond part of this thesis pays attentions on making decisions of spinning reserve markets.It builds a multi-objective optimization model with two objectives: expectation andconditional value at risk of tatal cost, which consists of costs of reserve capacity, outagelosses and the cost of elastic demands. The proposed model is solved with the improvedmulti-objective immune algorithm and a risk decision-making method based on fuzzytheory. In order to identify the reserve demand of renewable generations from the totaldemand of power system, this section carries researches on the reserve allocation of powersystem with renewable generations. It proposes a new method to allocate the total reserveby proposing a concept of reserve demand contribution. Furthermore, the reserve plansdecided by risk management tools will face a problem of tail risk which is with smallprobability but large losses, so the third part of this section proposes a method to solve thetail risk by covering insurances which can offer a choice for power system to change theuncertainty losses into a fixed losses by paying insurance fees.In order to promote technological advance of RGs, many countries give them somegenerating allowance and put renewable generations into the competitive markets. Theowners of RGs will bear the risks caused by their uncertainties in this situation. So thethird section of the thesis focuses on the optimization of RGs under a competitive market.It chooses two types of RG owners as the research objective: one is the microgrid withRGs and the other is the large scale renewable energy power producers. It first proposes amulti-objective short-term optimization model for those microgrids with the considerationof risk aversion by energy storage and solves it with a improved multi-objective immunealgorithm and fuzzy decision theory. Then it proposes an optimal bidding model for largescale RGs with the conditional value at risk or the optimal value of profits as risk measures.And in order to satisfy the demand of decision makers who want make decisions with theirown risk experiences, the last part of this section puts forward a new decision model basedon the prospect theory which enriches the short-term optimization model of large scaleRGs.