Optimal Allocation of Regional Integrated Energy System Considering Topology Planning

With the promotion of energy internet, the development of integrated energy system and the emergence of various energy storage modes, the establishment of the operation mode of integrated energy station with multiple energy storage modes of electricity, gas, cooling, and heating can improve the utilization rate of distributed new energy. Based on the infrastructure and model of regional integrated energy system, this paper studied the collaborative optimal allocation of electricity storage, electricity to gas (P2G) technology, cooling storage and heating storage under the multi energy complementary coupling operation of CCHP units. And a multi energy storage and low-carbon economic optimal allocation model of regional integrated energy system was put forward. Finally, the benefits of energy storage equipment in regional integrated energy system were quantitatively analyzed through economic indicators and carbon emission indicators. The example results showed that the regional integrated energy system with four energy storage modes of electricity, gas, cooling and heating can absorb more distributed wind and solar new energy, improve economic benefits and reduce carbon emissions.

In recent years, with the in-depth advancement of the rural revitalization strategy, the vast rural areas have continued to develop in all directions. However, in the field of power and energy, problems such as irrational energy structure, low energy development and production levels, and lagging development of energy system planning and construction still exist. At the same time, the Regional Integrated Energy System (RIES) is increasingly regarded as a key technology for promoting multi-energy complementation and optimizing the energy structure. The abundant solar energy in rural areas of my country has played an important role in RIES. In view of the widespread problem of poor system performance due to the limited grid-connected capacity of distributed photovoltaics(DPV), based on the equipment selection and capacity configuration of conventional RIES planning, this paper proposes a method to consider DPV on-site supply Energy's comprehensive energy system planning method has improved RIES' comprehensive performance in terms of unit energy supply cost, equipment utilization, initial investment and recovery period, and energy onsite supply rate.

Cost-based siting and sizing of energy stations and pipeline networks in integrated energy system

Ancillary services are critical to maintaining the safe and stable operation of power systems that contain a high penetration level of renewable energy resources. As a high-quality regulation resource, the regional integrated energy system (RIES) with energy storage system (ESS) can effectively adjust the non-negligible frequency offset caused by the renewable energy integration into the power system, and help solve the problem of power system frequency stability. In this paper, the optimization model aiming at regional integrated energy system as a participant in the regulation market based on pay-for-performance is established. Meanwhile YALMIP + CPLEX is used to simulate and analyze the total operating cost under different dispatch modes. This paper uses the actual operation model of the PJM regulation market to guide the optimal allocation of regulation resource in the regional integrated energy system, and provides a balance between the power trading revenue and regulation market revenue in order to achieve the maximum profit.

Under the dual pressure of energy and environmental crisis, planning a regional integrated energy system (RIES) is of more practical significance. This paper proposes a capacity planning method that can be applied to the electricity-cold-heat-gas coupling system to achieve the optimal configuration of the RIES. The RIES studied in this paper includes distributed renewable energy, multiple energy storage devices, and coupling equipment such as combined heat and power (CHP) and heat pumps. It not only meets the demand of cold, heat, and electricity load but also improves the economic benefits of the system. First, the model of RIES is established and well described according to the selected devices and system energy supply structure. Then, take the minimum annual economic cost of the planning year as the objective function. The equipment selection and capacity configuration are optimized utilizing mixed integer linear programming (MILP). Finally, the remarkable role of this planning method in optimizing system economic operation is verified through case comparison under different scenarios. The typical scenario analysis also studies the optimization effect of the absorption chiller and multi-energy storage system.

통상적으로 표준적인 최적배분은 층별 조사비용을 고려하여 표본을 배분한다. 만일 조사단위당 비용이 서로 다를 경우 보다 현실적인 배분방법을 고려할 필요가 있다. 즉, 개별 조사단위의 특성에 따라 이익비용비를 최대로 하는 단위를 먼저 표본으로 고려하는 동적배분을 고려하였다. 이러한 관점에서 층별 표본수를 배분하고, 배분된 표본규모에 따라 임의로 표본을 선정하는 방식인 표준적인 최적배분과는 차이가 있다. 이 논문은 약물오용, 낙태, 알콜중독 등과 같은 민감한 특성을 조사하는 층화확률화 응답기법에 대해 각 층별로 표본을 배분할 경우 최적 동적배분을 고려하여 보다 현실적인 문제를 해결하고자 하였으며, 수치적 예제를 통해 동적배분 방법의 효과성을 증명하였다. Typically the standard optimal allocation method distributes the sample for each stratum considering survey cost. In case of varying survey cost for each survey unit, we need to consider more practical allocation method. In other words, according to characteristics of an individual unit, we consider the optimal dynamic allocation method which first selects the survey unit having maximum value of benefit cost ratio. In terms of this, the proposed allocation method is different from standard optimal allocation method which allocate samples for each stratum and selects the random sample according to each size of sample. This paper is considered the dynamic optimal allocation method for the stratified randomized response technique which surveys for sensitive characteristic of survey units such as drug abuse, abortion, alcoholic. We prove the practical usefulness of proposed method using the numerical example.

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

With the availability of advanced MPSoC and emerging Dynamic RAM (DRAM) interface technologies, an optimal allocation of logical data buffers to physical memory cannot be handled manually anymore due to the huge design space. An allocation does not only need to decide between an on-or off-chip memory, but also needs to take an increasing number of available memory channels, different bandwidth capacities and several routing possibilities into account. We formalize this problem and introduce a Mixed Integer Linear Programming (MILP) model based on two different optimization criteria. We implement the MILP model into a retargetable tool and present a case study with representative data of the Long-Term-Evolution (LTE) standard to show the real-life applicability of our approach.

Optimal effort allocation in stratified, large area trawl surveys, with application to shrimp surveys in the Barents Sea

This paper proposes a new reliability optimization allocation for multifunction systems with multistate units based on goal-oriented (GO) methodology. First, this optimization allocation method is expounded in terms of establishing GO model, establishing reliability optimization allocation model, and solving algorithm. Then its process is formulated. Finally, the new method is applied in reliability optimization allocation of power-shift steering transmission (PSST), whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indices of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems with multistate units.

To solve the question of guaranteeing the safety of a system with a complex structure and tight couplings, a new method of optimal allocation exploiting the dynamical planning theory is presented. Two key factors that can affect system safety, i.e., accident loss and accident rate are analyzed. Then, to measure comprehensively requirements of system safety, accident safety level is defined based on these two factors. Subsequently, optimal allocation for complex systems’ safety resource is implemented according to the network properties of the inherent structures of the systems and constraint conditions for safe investments. Finally, a case study shows that the method using dynamical planning for optimal allocation of system safety can ensure preferential allocations of the parts with high safety requirements. Besides, this optimal allocation method is feasible and valid.

Co-firing of biomass in fossil fuel-fired power plants is a mature technology option for reducing greenhouse gas emissions. Up to 10 % of fossil fuel energy input can be displaced by biomass without the need for major retrofits. Co-firing may be done directly or indirectly. However, direct co-firing has the disadvantage of being less flexible with the use of different types of biomass. Indirect co-firing, on the other hand, overcomes this limitation by using a gasifier which converts the biomass into syngas and biochar. The syngas can then be used for co-firing, while biochar can be applied to soil as a form of carbon sequestration. This process stores the carbon initially fixed by biomass through photosynthesis in the soil, and results in the net transfer of part of the carbon in biomass from the atmosphere to the soil. Biochar thus acts as a negative emissions technology with potential for scale-up in the near future. The utilization of biomass for co-firing in a fleet of thermal power plants can be optimized as a carbon management network, subject to biomass availability and the presence of suitable biochar sinks. In this work, a fuzzy mixed integer linear programming model is developed to minimize net carbon emissions in such a system, taking into account parametric uncertainties in the storage capacities of the biochar sinks. It is assumed that there is the option to use direct, indirect or no co-firing in each power plant in the system. The model is illustrated using a case study.

The integrated energy system creates the possibility for the interconnection and coordination of different energy sources, and is an effective means to improve the energy use of the system, increase energy efficiency, and reduce environmental pollution. At present, the planning of distributed energy stations for integrated energy systems mostly focuses on equipment selection and equipment capacity. However, there are relatively few studies on the location of energy stations and pipeline layout planning. Firstly, this paper proposes a distributed energy station location method based on the improved p-median model, which combines the energy supply path of the energy station with the actual transportation network, and introduces the weight coefficient of multi energy load to reflect the diversity of energy demand of load. Finally, the specific solution method is given, and the rationality and feasibility of the proposed method are verified by an example.

An evolutionary algorithm-based optimal allocation method of wind resources under the background of carbon neutralization is proposed in order to better achieve the goal of energy conservation and emission reduction under the background of carbon neutralization, aiming at the current unreasonable allocation of wind resources. The evaluation model of balanced wind resource allocation is designed, and the evaluation index of optimal wind resource allocation is constructed using the evolutionary algorithm. The optimal allocation path of wind energy resources is chosen to achieve the goal of reasonable wind energy resource allocation. Finally, simulation experiments show that using an evolutionary algorithm to solve the problem of poor energy allocation and achieve the research goal, the optimal allocation method of wind energy resources under the background of carbon neutralization can effectively solve the problem of poor energy allocation.

Advanced metering infrastructure targets the real-time collection of massive multisource data with smart meters (SMs), forming a key component of active distribution networks (ADNs). To achieve comprehensive situation awareness for ADNs, the use of high-speed and reliable two-way communications, such as 5G communications, is the basis. However, it is challenging to optimally allocate wireless resources for massive data, especially when emergency events occur randomly. This study discusses an energy-efficient and optimal wireless resource allocation method employing 5G uplink-based machine-type communications for achieving ADN situation awareness. We propose a 5G-based framework that can reasonably reserve resource blocks for emergency data and model the resource allocation scheme as a problem of maximizing energy efficiency (EE). Then, by using the sample average approximation theory, we transform the stochastic constraint and summarize the scheme as a mixed-integer linear programming problem. Finally, we construct an iterative algorithm based on the Lagrange dual algorithm for searching the global optimal allocation scheme. The results of simulations and experiments illustrate that even if emergency events occur randomly, our algorithm can optimally support the cooperative transmission of all sampled data while providing the best EE for large-scale SM communications.